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Theory and application of the momentary fourier transform Albrecht, Sandor 1998

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THEORY AND APPLICATION OF THE MOMENTARY FOURIER TRANSFORM by Sandor Albrecht M . S c . E . E . Technical University of Budapest, Hungary, 1993  A THESIS S U B M I T T E D I N P A R T I A L F U L F I L L M E N T O F THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in T H E F A C U L T Y OF G R A D U A T E STUDIES DEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING W e accept this thesis as conforming to the required standard  T H E UNIVERSITY OF BRITISH C O L U M B I A September 1998  © Sandor Albrecht, 1998  In  presenting this  degree at the  thesis  in  partial  fulfilment  of  the  requirements  University  of  British  Columbia,  I agree that the  for  an  advanced  Library shall make it  freely available for reference and study. I further agree that permission for extensive copying of  this thesis for scholarly purposes may be  granted by  the  department  or  understood  that  by  his  or  her  representatives.  It  is  head of my  publication of this thesis for financial gain shall not be allowed without permission.  Department The University of British Columbia Vancouver, Canada  DE-6 (2/88)  copying  or  my written  Abstract  The discrete Fourier transform (DFT) is a widely used tool in signal or image processing and its efficiency is important. There are applications where it is desirable to use relatively small, successive, overlapped D F T s to obtain the spectrum coefficients. The momentary Fourier transform ( M F T ) computes the D F T of a discrete-time sequence for every new sample in an efficient recursive form. In this thesis we give an alternate derivation of the M F T using the momentary matrix transform ( M M T ) . Recursive and non-recursive forms of the inverse M F T are also given, which can provide efficient frequency  domain manipulation (e.g. filtering). Discussion on the properties  and  examples of the usage of the M F T is given, followed by a survey on its efficiency.  In this work we investigate the applicability of the M F T to synthetic aperture radar (SAR) signal processing, and in particular show what advantages the M F T algorithm offers to the SPECtral ANalysis ( S P E C A N ) method and burst-mode data processing. In the S P E C A N algorithm, the received signals are multiplied in the time domain by a reference function, and overlapped short length D F T s are used to compress the data. The azimuth F M rate of the signal varies in each range cell, which leads to the issue of keeping the azimuth resolution and output sampling rate constant. After the introduction to S P E C A N , we show what advantages and disadvantages  the M F T has compared to the F F T  algorithms.  ii  When a S A R system is operated in burst-mode, its azimuth received signal has a segmented frequency-time energy in its Doppler history. It requires that EDFTs be located at specific points in the spectral domain to perform the azimuth signal compression. After the introduction of the burst-mode data properties, we show why the short EFFT (SIFFT) algorithm has the requirement of arbitrary-length, highly-overlapped EDFTs to process burst-mode data, in which case the I M F T is shown to have computational advantages.  111  Contents  Abstract  ii  Contents  iv  List of Tables  vii  List of Figures  viii  Acknowledgements  x  1 Introduction  1  1.1 Background  1  1.2 Thesis objectives and outline  4  2 Theory and Properties of the Momentary Fourier Transformation * 2.1 Introduction  6 6  2.2 The Momentary Fourier Transformations derived form the Recursive Momentary Matrix Transformation  6  2.2.1  The Recursive Momentary Matrix Transformation  7  2.2.2  The diagonal form of the M M T  9  2.2.3  Inverse of the diagonalized M M T  2.2.4  Momentary Fourier Transform  2.2.5  The non-recursive Inverse M F T  11 ,.  14  2.3 Properties of the M F T 2.3.1  Cosine windows using M F T  2.3.2  Implementation of the M F T algorithm  2.3.2.1 Software Implementation of the M F T  12  16 ;  16 ,  19 19  iv  2.3.2.2 Hardware Implementation of the M F T 2.3.3  Example of M F T Usage  2.4 Computing Efficiency of M F T  21 25 27  2.4.1  Arithmetic of M F T  27  2.4.2  Comparison of M F T to F F T algorithms  28  2.4.3  Advantages and Uses of M F T  33  3 Overview of SAR Processing 3.1  Introduction  35 35  3.2 Ideal point-target model  35  3.3  39  S A R signal compression  4 Application of MFT to SPECAN SAR Processing Algorithm  42  4.1 Introduction  42  4.2 The S P E C A N Algorithm  42  4.3 M u l t i look processing in S P E C A N  48  4.4 The S P E C A N Algorithm Using the M F T  50  5 Application of MFT to Burst-mode SAR Data Processing  62  5.1  Introduction  62  5.2  Burst-mode S A R processing  62  5.3 Properties of fully exposed targets in burst-mode processing  64  5.4 Properties of partially exposed targets in burst-mode processing  67  5.5 The SLFFT Algorithm  69  5.5.1  Number of good output targets of a single EFFT  72  5.5.2  Real data simulation of burst-mode processing  74  v  5.6  Efficiency of SJFFT using the I M F T and the EFFT algorithms  77  5.6.1  Effect of varying S A R parameters and SNR/efficiency tradeoffs  78  5.6.2  Arithmetic of the SEFFT algorithm using the I M F T and the EFFT algorithms 80  6 Conclusions  89  6.1  Summary  89  6.2  Future work  91  Bibliography  93  vi  List o f Tables  Table 1 Memory requirement of the M F T  20  Table 2 Real operations in M F T for 7V coefficients C  28  Table 3 Resolution versus D F T length in S P E C A N for C-band satellite S A R  48  Table 4 Spaceborne and airborne S A R parameters for S P E C A N arithmetic calculation. 52 Table 5 Reduced and full M F T versus mixed-radix F F T  56  Table 7 Envisat swath parameters  78  Table 8 M i n i m u m and maximum burst bandwidth of the seven swathes  79  Table 9 The length of the JTJFTs and the corresponding d S N R  84  List of Figures  Figure 1 Windowing of the discrete-time function  7  Figure 2 B l o c k diagram of the full M F T algorithm  22  Figure 3 Block diagram of the full M F T algorithm without the modulo-TV F I F O  23  Figure 4 Hardware structure of one M F T block  24  Figure 5 F S K signal analysis using M F T  25  Figure 6 Signal detection using M F T  26  Figure 7 Shift between D F T s when the M F T is more efficient  29  Figure 8 Arithmetic of M F T and Radix-2 F F T when qMvr =1  30  Figure 9 Arithmetic of M F T and Radix-2 F F T when q rr = 4 M  30  Figure 10 Floating Point Operations of D F T algorithms  31  Figure 11 Fast Convolution with M F T and Radix-2 F F T  33  Figure 12 Synthetic aperture radar geometry  36  Figure 13 Deramping of multiple targets  43  Figure 14 Processing regions and the placement of successive D F T blocks in single look case  45  Figure 15 Division of the good output samples into looks and the location of the D F T operations in multi-look processing Figure 16 Azimuth F M rate and the D F T length with varying range  49 51  Figure 17 Arithmetic of S P E C A N azimuth compression with different D F T algorithms 55 Figure 18 The arithmetic of S P E C A N when the M F T is more efficient  58  viii  Figure 19 The arithmetic of the Range-Doppler algorithm and the S P E C A N algorithm using the M F T and F F T algorithms  58  Figure 20 The output sampling rate of the S P E C A N algorithm  60  Figure 21 Burst-mode operation in 2-beam S c a n S A R case  63  Figure 22 Burst-mode processing of 16 fully exposed and evenly spaced targets in one range cell  64  Figure 23 Burst-mode processing of fully and partially exposed targets in one range cell 66 Figure 24 Effect of the circular convolution on the targets Doppler history  68  Figure 25 H o w minimum and maximum EDFT is placed to compress groups of targets from each burst  71  Figure 27 The Doppler history of real burst-mode data  76  Figure 28 Burst bandwidth and dSNR of IS1 swath  80  Figure 29 Arithmetic of the S I F F T algorithm when applied to the IS1 swath  86  Figure 30 Arithmetic of the S I F F T when it is applied to Envisat A P burst mode operation 88  ix  Acknowledgements  First of all, I would like to thank my Mother and Edina for providing consistent support and encouragement throughout my studies at U B C . Without their love and care this work would not have been completed.  I would like to thank my supervisor, D r . Ian Cumming, for his supervision and academic guidance, as well as providing the opportunity to continue my research of momentary Fourier transform in the field of synthetic aperture radar processing.  I am grateful for the financial support provided for this research by the Natural Sciences and Engineering Council of Canada and by MacDonald Dettwiler and Associates.  Finally, I would like to thank all of the members of the Radar Remote Sensing Group at U B C for providing a friendly and effective work environment.  x  Chapter 1  Introduction  1.1  Background  Linear transformations, such as the discrete Fourier transform ( D F T ) are frequently used in digital signal processing, and their efficiency is very important. In applications where the D F T is applied to a signal, it is often desirable to use successive, possibly overlapping D F T s of smaller extent than the full length of the signal to obtain the spectrum coefficients. These transformations are normally off-line operations on blocks of data, requiring N samples of the signal before the transformation can be computed. The momentary Fourier transform ( M F T ) which is derived here is a method of computing the D F T of a sequence in incremental steps. It can be computed using an efficient recursive formula, and it is useful in cases where the detailed evolution of the spectra of a discrete series is wanted, and in cases where only a few Fourier coefficients are needed.  The spectrum components of the M F T can be calculated independently and only one complex multiplication and two complex additions are needed to update each spectrum component. The inverse momentary Fourier transform ( I M F T ) is the dual of the M F T and  1  shares the same property, while the non-recursive form of the I M F T requires only additions to obtain a sample of the time sequence from its spectrum with N samples delay.  The computational order of the M F T to update an TV-point D F T is N, a factor of log2N improvement over the radix-2 F F T algorithm i f all incremental results are needed. If only a sub-set of the transform domain components are needed, the computing load of the M F T can be further reduced, calculating only the coefficients of interest. The M F T does not rely upon on N being power of two to obtain its efficiency, in contrast to standard F F T algorithms.  Uses of the incremental D F T were introduced by Papoulis in 1977 [1], and by Bitmead and Anderson in 1981 [5]. A detailed derivation of the momentary Fourier transform was given by Dudas in 1986 [6]. In 1991, L i l l y gives a similar derivation, using the term "moving Fourier transform", and uses the M F T for updating the model of a time-varying system [7]. In this thesis we further develop the theory of M F T , examine its applications and in particular, see what advantages it offers to synthetic aperture radar  data  processing.  A synthetic aperture radar ( S A R ) is a powerful sensor in remote sensing which is capable of observing geophysical parameters of the Earth's surface, regardless of time of day and weather conditions [3]. S A R systems are extensively used for monitoring ocean surface patterns, sea-ice cover, agricultural features and for military applications such as in the  2  detection and tracking of moving targets. A S A R transmits radar signals from an airborne or spaceborne antenna which is perpendicular to the flight direction of the platform which is travels at a constant velocity. The back-scattered signal is collected by the antenna and stored in a raw format. Extensive signal processing is required to produce the output S A R image.  The SPECtral ANalysis ( S P E C A N ) S A R processing algorithm was developed in 1979 by MacDonald Dettwiler and Associates, as a multi-look version of the deramp-FFT method of pulse compression. In S P E C A N , the received signals are multiplied in the time domain by a reference function, and overlapped short length D F T s are used to compress the data. In contrast, a precision processing algorithm such as the Range Doppler (RD) method requires both forward and inverse D F T operations, thus it is less computationally efficient. S P E C A N is an efficient algorithm for moderate to low resolution processing and generally implemented in quick look processors for viewing of magnitude detected imagery data.  Burst-mode operation is used in S A R systems, to image wide swaths, to save power or save data link bandwidth. Several spaceborne remote sensing missions employ the ScanSAR mode in addition to other operational modes for radar imaging. Canada's Radarsat satellite, which was successfully launched in 1995, is a sophisticated Earth observation system developed to monitor environmental changes. The imaging platform supports various S A R operating modes, including a S c a n S A R mode for the lowresolution (~100m) imaging of ground regions of width 500 k m .  3  A n Advanced S A R ( A S A R ) system w i l l be flown on the Envisat satellite polar platform to be launched in 2000 by the European Space Agency. This system w i l l be able to operate in three burst-modes: alternating polarization mode (AP), wide swath mode (WS) and global monitoring mode ( G M ) . Alternating polarization mode provides high resolution in any swath with polarization changing from sub-aperture to sub-aperture within the synthetic aperture. This results in two images of the same scene in different polarizations combination with approximately 30 m resolution. In the wide swath mode the ScanSAR technique is used providing images of a wider swath (405 km) with medium resolution (150 m).  1.2  Thesis objectives and outline  The objective of this research is to further develop the theory of the M F T , examine its properties and applications, and in particular, see what advantages it offers to S P E C A N processing and to the short I F F T (SIFFT) burst-mode processing algorithm.  Chapter 2 presents the theory and properties of the momentary Fourier transform. Here, we introduce the recursive form of the momentary matrix transform ( M M T ) , and show when the M M T takes the form of the D F T or the I D F T , the resulting M F T and EvLFT have an efficient computational structure. The properties and computing efficiency of the M F T is also discussed in this chapter.  4  In Chapter 3, an overview of SAR processing is given, where the conventional compression method of the SAR signals is studied. This chapter gives a background knowledge for the research of the SPECAN algorithm and burst-mode data processing.  The azimuth FM rate of the received signal varies in each range cell, which leads to the issue of keeping the azimuth resolution and output sampling rate constant. After the introduction of the SPECAN algorithm in Chapter 4, we show what advantages the MFT method offers vs. the FFT algorithms when they are applied to the SPECAN SAR processing algorithm.  In Chapter 5, the ScanSAR operation mode will be introduced, and the received burstmode data properties will be analyzed. After the effect of the varying SAR parameters and SNR/efficiency tradeoffs, a survey on the arithmetic of the SIFFT algorithm using IMFT and EFFT is given. Here, we show that the JJVEFT algorithm can improve the computational efficiency of the SIFFT algorithm in certain burst-mode data processing cases.  Finally in Chapter 6, conclusions of the efficiency and applicability of the momentary Fourier transform to SAR processing will be drawn, and suggestions for possible future work will be given.  5  Chapter 2 Theory and Properties of the Momentary Fourier Transformation  2.1  Introduction  In this Chapter, we give a derivation of the momentary Fourier transforms from the momentary matrix transform in Section 1.2. Section 1.3 gives a survey on the properties of the M F T , and in Section 1.4 a discussion on its computational efficiency is given.  2.2  The Momentary Fourier Transformations derived form Recursive Momentary Matrix Transformation  the  In this section, we introduce the matrix form of the momentary transform, and show that it has a recursive form. W e also show that when the momentary matrix transform takes the form of the D F T or the inverse D F T , the resulting M F T has an efficient (recursive) computational structure. In the last part of the section, the inverse of the M F T is introduced, as well.  6  2.2.1 The Recursive Momentary Matrix Transformation Let xi be a sample of an arbitrary complex-valued sequence of one variable. The sequence w i l l be analyzed through an TV-point window, ending at the current sample i. In subsequent analyses, the window w i l l be advanced one sample at a time. A t sample i x, enters the window, while Xi. leaves the window, as shown in Figure 1. N  Samples  Figure 1 Windowing of the discrete-time function At samples i-1 and i, the windowed function can be represented by the following two column vectors:  X, =  v  v  i-i  i-i  X,  (1) 7  Let T be an NxN non-singular matrix, which represent a linear transformation and has the inverse T" . The sequence of index vectors is transformed by T at each sample:  • • • - y,-i =Tx ._„ y,. =Tx,., . . . 1  (2) Let P be the NxN elementary cyclic permutation matrix. When the vector  is  multiplied by P, a one-element circular shift is performed, such that the index of each element is increased by one, and the first element becomes the last one:  0 , where  1  .  .  0  .  0  1  .  0  P= .  .  0  1  .  . . . 0 1 1  0  .  .  0  x,  (3) Using the result above, the x; vector can be expressed by the shifted xj.i vector and with an adjustment vector Ax\ made from the difference between the samples entering and leaving the window: 0  i-(N-\)  X  + X  Px  w  +AXi  0  i-l  i-N  X  =  _ i~ i-N X  X  _  (4)  8  Substituting (4) into the transformation associated with the ith window in (2) and using the inverse transform XM = T" V M , the following relationships are obtained: 1  y, =Tx, =T[Px,_ +Ax,.]=TPT- y,_ +TAx I  1  1  i  (5) Equation (5) expresses the recursivity of the momentary matrix transforms (MMT), since calculation of the newly transformed index vector v* needs the previously transformed index vector  2.2.2  and the difference between samples entering and leaving the window.  The diagonal form of the M M T  The momentary matrix transform is particularly efficient and it can be calculated by components only i f the product of similarity matrix transform TFT" in (5) is diagonal. 1  The P matrix has N distinct eigenvalues (Ao, roots, X = w~ - e k  ,k-0,1,2,...N-1.  j2nkJN  k  ^N-I) which are the nth complex unit  T o each eigenvalue, N linearly independent  eigenvector corresponds as follows:  w A = w°=l <=> s = 0  0  w  w  (N-l)  9  1  w  w X =w  <=> s =  k  k  t  -2k  w  ; -  >•  K-i=  w  ' '  <=>  (N-l)k  W  w  V i =  w  2(N-\;  •(N-l  )(N-l)  (6) If the eigenvectors are chosen to be the columns of the inverse of the T matrix, then TPT" is a diagonal matrix, with the eigenvalues of P along its diagonal: 1  0 0  TPT  1  = S PS =  S  n  S,  s'  1  3  N- 2  -A,  s" s  0  0 A  1  2  0  N-l  I  I  0  0  .  . A .  (7)  where S is the eigenvector matrix of P.  The diagonalizing matrix S is not unique. A n eigenvector s can be multiplied by a k  constant, and w i l l remain an eigenvector [2]. Therefore the columns of S can be multiplied by any nonzero constants and produce a new diagonalizing S. There is also no preferred order of the columns of S. The order of the eigenvectors i n S and the eigenvalues i n the diagonal matrix is automatically the same. Therefore, all T matrixes, which satisfy the above mentioned properties w i l l diagonalize the momentary matrix transform:  10  X  0  .  .  0  0  A,  0  .  0  k  0 0 0  0 (8)  where k, I, m e {0,1,...,N-l)  and T _i is the last column of the T matrix. N  2.2.3 Inverse of the diagonalized M M T If y i is available at each sample and the columns of T are the eigenvectors of P , an efficient implementation of the inverse of the M M T can be obtained. The inverse MMT (IMMT) at time i:  (9) HNi)  X  11 r y,. ^  1 1 1 1 1  0  (10) The first row of T" contains only ones (10), so the oldest element of X ; can be computed 1  using adds only: AM  i-(N-l)  k  =  0  (11) 11  from which the elements of the input sequence (JC,.^.;j.. .JC,) can be computed from the transform domain sequence y; with N-l sample delay.  2.2.4 Momentary Fourier Transform The matrix of the Discrete Fourier Transform ( D F T ) and the Inverse Discrete Fourier Transform (EDFT) have the properties described in Section 1.1.2, thus their columns are the eigenvectors of the matrix P:  1 N-l  w  w  W  2  W  DFT = F = S w  2iN-\)  W  2-(N-l)  (N-\HN-\)  (12)  1  1 „-<N-\)  w  IDFT= F = S =  w  1  -Z(N-l)  W  N -iN-l,  w  HN-\HN-l)  -l<N-l>  l  w  1 N-l  W  w  _1_  W  2(N-l)  W  2 W  N JN-lXN-l)  „2(N-l)  (13)  12  Using that w is the Nth complex unit root (i.e. w' = w ' ), it can be seen that the columns k  N k  of the I D F T matrix are the same as the D F T matrix, but they are in reverse order from the second column (13). Therefore, i f T performs the D F T (14) or the I D F T (15), diagonal forms of the M M T can be obtained:  1 0 0 w  0 0  0  A  y,.= F P F - ' y ^ + F A x , =  w'  2  w  0  y,-. +  w  -2 (  X  l -  X  i -  N  )  0 0  0  w  -(N-l)  W  -(N-l)  (14) 1 0 0 w  1  x^F-'PFx^+F-'Ay,. =  0 w  2  I  0 0  0  w  2  (y,•-y,, ,)= A  0 0  0 . .  1 0  0  w  N-l  0  w  (NA)  w  {N-l)  Q  w  N-l  0" 0  w -(N-l)  .  w  1 (N-2)  ( y i - y i - s )  0 0  0  w  -1  W  (15) Equation (14) expresses the recursive equation of the momentary  Fourier-transform  (MFT). The TV-element vector y; contains the Fourier coefficients of the TV-point sequence xj ending at sample i. Note that each spectral component  can be calculated  independently,  13  y  i.k  =w  k  (yi-u  +  i- ,  x  x  ) (16)  which increases efficiency i f only a few frequency components need to be computed, as in the zoom transform.  On the other hand, equation (15) is the dual pair of the M F T , the recursive inverse momentary Fourier-transform  (IMFT), where the TV-element vector x; contains the TV-  point time sequence and y-, contains TV Fourier coefficients ending at frequency bin /. Note that the each sample in X i can also be obtained independently and the same twiddle factors, but in different order, can be used to calculate both the M F T and I M F T .  Thus it has been showed that i f the D F T or the EDFT performs the momentary matrix transform of a sequence the elements of the transformed sequence can be computed recursively and independently using TV complex multiplies and N+l  complex adds  (computational savings are available i f the input sequence is real-valued).  2.2.5  The non-recursive Inverse MFT  The non-recursive inverse momentary Fourier transform can be expressed using (11) and  (13) as follows:  x  i-(N-l)  i,k  (17)  14  from which each sample of the input sequence (XJ) can be computed using adds only from the spectrum (y0 with N-l  sample delay. In this way the M F T - non-recursive I M F T  transform pair can provide an efficient frequency-domain manipulation method (e.g. filtering), especially i f many of the D F T coefficients are not calculated.  If the elements of x; are real, taking advantage of the conjugate symmetry of the spectrum, the oldest element can be computed using only the real part of the spectrum components:  t-(N-l)  N  k=0  (18) It has been showed in [6] that i f X; is real, the Hilbert transform of Xj.(N-i) can be obtained to sum only^the imaginary part of the spectrum components:  H{x  1= i-(N-l)  1  N-l  r  -,  -Hm{y. } k  N  k=0  (19) In this case the MFT/non-recursive I M F T pair can be useful for different signal processing applications where the in-phase (I) and quadrature component (Q) of the signal is needed (i.e. communications and radar systems).  15  2.3 Properties of the M F T In this section, some properties of the M F T are given. Section 1.3.1 shows how to implement cosine windows in the frequency domain using the M F T . In Section 1.3.2, discussion on the software and hardware implementation of the M F T algorithm is given, while section 1.3.3 gives an example of the use of the M F T .  2.3.1  Cosine windows using MFT  The TV-point D F T treats the data sequence as i f it has a periodicity of N samples, xi = for all integer  Xi+kN,  k.  In practice many signals do not have the above periodicity. If the  boxcar window is applied to such a signal, the D F T w i l l treat it i f there were discontinuities at its edges. Ringing effects near the edges of filtered signals may occur as a result of these spurious discontinuities [4]. Such effects can be reduced by applying a more appropriate window. In addition to selecting a portion of the input sequence, the window modifies this portion to make it continuous at the edges when regarded as periodically repeated. Several types of window have been described i n the literature [1], [4]. This section introduces how the Planning, Hamming and Blackman window can be implemented using the M F T .  Given the discrete-time sequence xj, we wish to calculate the M F T of the windowed data x  wJ  =  Wj-xi  at time i, where  wi  is the window function. The ith element of the window  may be expressed as follows:  Hanning:  w,= 0.5 1-cos  ( 2ni { N  J 16  Hamming:  w,= 0.54 - 0.46 cos  Blackman:  w.= 0.42 - 0.5 cos  ( 2ni  \  ( 2ni ^ N  v  Ani ^  r  + 0.08 cos  ,  N  v  j  (20) The derivation for the Blackman window is given below:  0.42 - 0.5 cos  =0.42 JC, -  0.04  exp  V  J  {  v  \  /  f  Ani ^ N  2ni ^ N  y j  x,- +  . Ani  exp  N  f  + exp  N  Ani\  f  + 0.08 cos  N  2ni  0.25 exp (  ( 2ni  N (21)  Taking the D F T of each part of (17) the spectrum of the windowed data at time i:  y  =  0.42y  wi, k  i,  0.25  y.  + yu-i  M  k  + 0.04  (22) Therefore, the M F T of the windowed data can be obtained simply by maintaining the M F T of the non-windowed data and applying a weighted average in the spectrum.  Similar results can be easily derived for the other cosine windows. These are: Hanning:  y  = 0.5 y  - 0.25 \y  ik  iM  + y _, ] it  wt,k  Hamming: y  wi,k  = 0.54 y  - 0.23 i,k  Lv  a+1  +y _ ik  l  \ (23)  17  Although only generalized cosine windows can be applied easily with the M F T , arbitrary windows can be approximated i f enough cosine terms are used. Note, that the memory requirement of the M F T algorithm gets larger, while its efficiency drops as the number of terms increases.  The edge effect of the boxcar window can also be compensated i f the non-weighted moving average of the spectrum components is used. The moving average of a spectrum coefficient at times i, for L ( L < N ) consecutive samples can be expressed as:  =  y mavg_i,k  1 ' T  L j=i-L+\  y i,k  (24) It also has a recursive form where, the calculation of the averaged spectrum coefficient needs the previously calculated average and the difference between the spectrum coefficient entering and leaving the averaging window:  y  =y mavg_i+l,k  +  ^ yt+\,k  yi-L+\,k ^  mavg_i,k  (25) The above defined moving average with the long-term average (26) of the M F T coefficients can also be useful for statistical analysis of the input discrete sequence. 1  y  k  =  k,avg  7 7  M s  ^ '  M  >  >  N  M i=l  (26)  18  2.3.2 Implementation of the M F T algorithm In this section, discussion on the software and hardware implementation of the M F T algorithm is given. Section 1.2.2.1 shows the computer coding of the M F T with its memory requirement, while the principle hardware structure of the M F T is given i n Section 1.2.2.2.  2.3.2.1 Software Implementation of the M F T A s it was shown earlier, the spectrum components i n the M F T algorithm can be calculated independently from each other. Thus, the M F T can be built up from identical blocks, where a block refers to equation (10). The software implementation of one M F T block can be obtained using the trigonometric form of the equation: y.  t  i,k  =w~*(y,_ + *,.-*,•_„) u  -j2nk  . ~ k  w  N e  = cos (<& }+- j sin ( ® ) k  k  -jink  where  N  k  (27) Re/y  } = cos(<Dj(Re/ y  J + Re{x x.  r  i,k  r  } = cos(oj(lm/ y  Im{y  J+lmfo-x^})  r  i,k  N  i-l,k  J)  +  sin (O )(Im{y k  i-l,k  }+  Im{x _ }) rXi  N  y" + R e 7 )  sin(oJ(Re/> i-l,k  (28)  Equation (28) corresponds to the kth M F T block for complex xi, where  is the kth  spectrum component at sample i. Re{} means real part and Im{} means imaginary part of a complex number.  19  The M F T blocks can be organized in a for loop to calculate the needed D F T coefficients. The following pseudo-code segment illustrates the computer coding of the M F T algorithm, assuming the sine and cosine arrays (twiddle factors) have been precomputed: calculate  (xi-Xi_ ) N  ;  f o r k = s t a r t t o s t a r t + N - l do MFTblock(k); endfor c  If the calculation of the spectrum coefficients is off-line, the difference of the entering and leaving samples of the window can be calculated for the whole data set and stored in a file or an array in the memory. If it is on-line, a modulo-A array is needed to calculate 7  xi-Xi-N. The index of the for loop in the pseudo-code indicates, that within the valid spectrum components only a smaller interval of the D F T coefficients can be computed. If the parameter start is zero and N = N , then all the spectrum coefficients are going to be c  calculated.  Within  the  MFTblock  procedure,  the  previously computed  spectrum  coefficient should be stored in an array for the computation of the recent one. The following table gives the memory requirement of the M F T algorithm:  Array type  Size  Twiddle factors (sine and cosine arrays)  2-NVB bits  Modulo-A^ F I F O for complex  2-N-B bits  Spectrum coefficients at time /'-/ - y,./ L  2 N B bits  Spectrum coefficients at time i - y,.;^  2-N -B bits  t  c  Table 1 Memory requirement of the M F T  20  In Table 1, parameter B is the number of bits used during the arithmetic operations, thus B = 32bits i f floating point arithmetic is used. B should be at least 24 bits for the fix point arithmetic, concerning the sensitivity of the M F T algorithm for the quantization error of the sine and cosine function.  Note, the memory requirement  of the M F T depends on the calculated  spectrum  coefficients. If the whole spectrum is computed, NB byte memory is needed for the computation.  2.3.2.2  Hardware Implementation of the M F T  The trigonometric form of the M F T for one spectrum component (29) can be easily implemented in hardware. From the basic blocks of M F T a parallel hardware structure can be built for the computation of the D F T coefficients. Figure 2 illustrates the block diagram of the concurrent implementation of the M F T blocks of the full M F T algorithm. Note, the updating time of the fully concurrent implementation is equivalent to the propagation time of one M F T block, regardless of the number of the calculated spectrum coefficients.  21  Figure 2 Block diagram of the full M F T algorithm  In Figure 2 the architecture of the full M F T contains a modulo-TV F I F O register to obtain Xi. . N  If all the spectrum coefficients are computed, the leaving sample of the window at  time i can be expressed using the I M F T algorithm: ^ X  i-N  N-l  = T7 X y.,, N to  "  lk  (29) Substituting (29) to (10), the recursive equation of one M F T coefficients becomes the following:  22  y  -w -k yt-ik + t x  (30) In (30), the data sample at time i and all the spectrum coefficients at time i-1 are needed to obtain y . The memory requirement of the M F T algorithm is reduced by 2-N-B bits, iik  because there is no need to save the input data samples in a F I F O , while the arithmetic of the M F T increased by 2N-1 real operations due to the calculation of I M F T . The block diagram of the M F T corresponding to equation (30) is shown in Figure 3.  MFT Block #0  M  MFT Block #T  x, - X,.  i"(N-1)  X  MFT Block #N-1  Yl.N.1  1/N  Figure 3 B l o c k diagram of the full M F T algorithm without the modulo-TV F I F O  The detailed hardware structure of one M F T block for complex Xi is given in Figure 4. This implementation contains four multipliers ( M P Y ) and four adders ( A L U ) to obtain  23  the complex arithmetic of the M F T . The twiddle factors and the previously calculated spectrum coefficients are stored in registers. Because of the parallel computation of the real and the imaginary part of the M F T coefficients, the updating time of the spectrum coefficients is limited only by the propagation time of two multipliers and two adders.  MFT Block #fc  sin(k2n/N)  cos(k2»t/N)  MPY  ALU  +  MPY  ALU  MPY  +  MPY  ALU  Re{Y  Re{x,-x,. } N  Mik  ALU  +  +  +  +  }  Re{Y, ) k  lm{Y } Lk  lm{x,-x,. } N  Figure 4 Hardware structure of one M F T block  24  2.3.3  E x a m p l e of M F T Usage  To illustrate the usage of the incremental form of the M F T , a frequency shift key ( F S K ) modulated sinusoidal signal of length 4N samples is used. Using an analysis window length 7V=100, and two frequencies of 5 cycles/window and 29 cycles/window, the magnitude of the evolving spectrum is shown in Figure 5, when the M F T is incremented by one sample at each analysis stage.  FSK Signal  0  50  100  150  200  Time  250  300  350  Time Dependent Spectrum of the FSK Signal  Figure 5 F S K signal analysis using M F T  The M F T begins with the initial conditions of yo = 0. This is equivalent to having N zeros precede the data vector. In Figure 5, note how the energy in the spectrum rises from zero to a maximum in the first /V samples. A l s o note how spectral leakage is observed in the  25  first N-l time samples, because the sinusoidal signal does not have an integer number of cycles/window over this time. A t time N, there is an integer number of cycles/window, so all the energy in the spectrum lies in one bin. For the next A M samples, leakage occurs again as the window sliding towards to the next frequency component of the signal. The spectral energy of the 5th frequency bin decays to zero while the spectral energy of the 29th bin rises to its maximum. This spectrum energy 'swapping' between the two frequency bins is repeated as the window is moving through the two frequency components.  In Figure 6, the same F S K signal is analyzed in the present of noise. The spectrum energy swapping between the two frequency bins is also noticeable, which shows how the M F T can be useful for signal detection in noise environment. FSK Signal in Noise  Time Dependent Spectrum ot the FSK Signal  Figure 6 Signal detection using M F T  26  2.4  C o m p u t i n g Efficiency of M F T  In this section, a survey on the arithmetic of the M F T is given, followed by a discussion of its efficiency. The M F T is particularly efficient compare to F F T algorithms, when successive D F T s with high overlap ratio are to be computed or when only a few spectrum coefficients are needed. Examples of applications of the M F T to signal processing is also given, here.  2.4.1  Arithmetic of MFT  The previously derived equation for one spectrum component of an N samples long M F T at time i: y.,  = ' (y .*+*/ w k:  M  The twiddle factors (w" ) can be calculated only once and stored in an array before the k  M F T procedure. This computation is not included in the arithmetic of M F T .  The difference of the sample entering and leaving the window -  XJ-X;_N  - can be pre-  calculated at each time moment and used for the calculation of all spectrum coefficients. In this case, the spectrum of X; can be updated from the spectrum of x\.\ using only N complex multiplies and N+l complex adds, i f x j contains complex-valued data. If Xj is real, N/2 complex multiplies  and N/2+1  real adds are needed to obtain the N / 2 new  spectrum coefficients. Table 2 gives a summary of the number of real operations for these  27  cases when only N coefficients are calculated ( N c  <N for complex data and N <N/2 for  c  c  real data).  Input data  Real Multiplies  Real Adds  Real Operations  Real  4N  C  3N +1 C  7N +2  Complex  4N  C  4N +2  8N +2  c  c  c  Table 2 Real operations in M F T for N coefficients c  Note, the number of operations in each case can be reduced with one, i f the D C component is calculated, because in that case the twiddle factor equals to 1 (w = 1 when k  k=0).  2.4.2 Comparison of MFT to FFT algorithms Consider the case where TV point D F T s are used to analyze an M-point complex-valued data record. If the window is shifted by q samples between each D F T application, where M-N  1 <q <N, then  +1 D F T s are needed to spectrum analyze the record, in the case q  of F F T . If the M F T is applied, M M F T s are needed, because the spectrum coefficients have to be calculated in each time samples, irrespectively of the value of q.  Then, when radix-2 F F T s are used: f  OPS  —  M - N  ^  + 1 [5Mog (iV)] 2  (31)  28  real operations, while in the case of M F T : OPS =M[8N +2] MFr  c  (32) real operations are needed to analyze the whole record.  From (31) and (32) the number of shift between D F T s when the M F T is more efficient than the radix-2 F F T can be expressed: (M QMFT  <  -N)[5N\og (N)] 2  M(SN -l)-5N\og (N) c  2  (33) A s we can see from (33), qMrr is function of the length of the data record (M), the size of the window (AO and the calculated M F T spectrum coefficients (N ). In Figure 7, the shift c  between D F T s when the M F T is more efficient is shown as a function of the window length, with two values of M and N : c  Number of shift between DFTs when MFT is more efficient than FFT  Number of shift between DFTs when MFT is more efficient than FFT 1  .  .  ,  .  .  ,  !  «  1  Total sample* an alyzed = 5000  Total samples analyzed: MFT  i i  r  |18 5 "16  t  a 14  » i>  .0  i  i  i  I:::*-  |  0  FullMFT  !  \  -  j<  0••! •  o 100  6  200  300  400 500 600 Window size [sample]  (a)  700  800  900  1000  100  200  300  400 500 600 Window size [sample]  700  800  900  1000  (b)  Figure 7 Shift between D F T s when the M F T is more efficient  29  The full M F T is more efficient compared to the radix-2 F F T , i f the shift between D F T s is very small (qMFT ^ 5), while for the reduced M F T (Nc = N/4), the M F T is more efficient even for larger values of shift. Note, i f the data record is longer (Figure 7 (b)), the values of qMFT are larger for all window sizes. The computational load for small amount of shifts is illustrated in Figure 8 and 9: Arithmetic of MFT and Radix-2 FFT - Shift Between DFTs = 1 sample(s)  1  — i  1  1  1  1  1  1  1  Arithmetic of MFT and Radix-2 FFT - Shift Between DFTs = 1 sample(s) Total samples ana|yzrf=M000  .Total samplesanalyzed.?. 5QO0,  400 500 600 Window size [sample]  400 500 600 Window size [sample]  700  (a)  700  (b)  Figure 8 Arithmetic of M F T and Radix-2 F F T when qMFT = 1  Arithmetic of MFT and Radix-2 FFT - Shift Between DFTs = 4 sample(s)  1  1  1  nples anilyzcd = flNM)  1  Radix-2 FFT \  1  1 1  / — ;  Full MFT  1/4NAUFT  --0-101)  200  _. 1  3(H)  1  • 1 500  1  41)0 600 Window size [sample]  (a)  Arithmetic of MFT and Radix-2 FFT - Shift Between DFTs = 4 sample(s)  1  J.4  1  700  800  900  1000  100  200  300  400 500 600 Window size [sample]  700  800  900  1000  (b)  Figure 9 Arithmetic of M F T and Radix-2 F F T when q FT - 4 M  30  The arithmetic of M F T is linear with the number of the computed spectrum coefficients (7V ) and the length of the data record (M). For a given record size (e.g. Figure 8 (a) and C  Figure 9 (a)) the M F T arithmetic remains the same, with varying shifts, while the F F T arithmetic drops down considerably as the value of shift gets larger. In Figure 10 the floating point operation per D F T is shown for radix-2 F F T , mixed-radix F F T , M F T and the direct D F T , when the consecutive windows are overlapped by N/2 samples (i.e. q Fr = N/2). The arithmetic of the mixed-radix F F T was estimated using the M  Matlab's fft and flops functions. The radix-2 F F T is very efficient i f the D F T length is power of two, while the M F T is more efficient when TV is a non-composite number (e.g. prime). Floating Point Operations per DFT  Window size [samples]  Figure 10 Floating Point Operations of D F T algorithms  When there is no overlap between the two consecutive D F T s (i.e. qMFT = AO during the spectrum analysis, the M F T has to be applied N consecutive times to obtain the next valid  31  D F T . In this case, the arithmetic of the M F T becomes the same as the direct D F T in Figure 10, while the arithmetic of the other algorithms remains the same.  A s an example of efficiency of the M F T , consider an TV-point frequency domain filter with a fast convolution method applied to the complex-valued data record. The filter coefficients are pre-calculated and stored and the filter is applied with a radix-2 F F T (or M F T ) , an array multiply, then an inverse F F T (or I M F T ) . When radix-2 F F T is used to obtain the convolution  (34) real operations are needed. In the case of the M F T the number of real operations needed are: COPS  MFT  =M[10N  C  +2] (35)  The comparison of efficiencies is shown in Figure 11:  32  Arithmetic of Frequency Domain Convolution  Window size [samples]  Figure 11 Fast Convolution with M F T and Radix-2 F F T  If the M F T is used to compute all the spectrum coefficients, then the radix-2 F F T is more efficient for most of the D F T lengths. But i f only a subset of the spectrum coefficients need to be computed (i.e. sub-band filtering), the M F T / I M F T transformation pair can be more efficient for many values of N.  2.4.3  Advantages and Uses of MFT  The computational order of the M F T to recursively calculate the coefficients of an Appoint D F T is N, a factor of log2N improvement over the F F T . If only a sub-set of the spectrum components are needed, the computing load of the M F T can be further reduced, calculating only the frequency coefficients of interest. The M F T does not rely upon on N being power of two to obtain its efficiency, in contrast to standard F F T algorithms. In this way, the M F T can provide more efficient computation of the D F T when any or all of the following conditions apply:  33  •  D F T s are highly overlapped  •  only a few Fourier coefficients are needed  •  a specific, non-composite D F T length is needed.  Concerning the above properties of the M F T , we can say that it can be useful in different applications of signal processing such as: •  on-line computations in real-time spectral analysis  •  on-line signal identification and detection  •  speech processing  •  radar and sonar processing.  34  Chapter 3 Overview of SAR Processing  3.1 Introduction In this chapter the basic geometry of the synthetic aperture radar ( S A R ) system, the mathematical form of the ideal received signal of a point target and the traditional S A R compression algorithm, the range-Doppler algorithm are introduced.  3.2  Ideal point-target model  Assume, the airplane or satellite carrying the S A R antenna travels across the surface of the earth at a constant velocity (V ) while transmitting microwave pulses at a given pulse r  repetition frequency (PRF) to the ground with a squint angle © as it is shown in Figure 12. The direction of travel of the S A R antenna is called the azimuth direction while the direction of travel of the transmitted pules is referred to as the range direction ( azimuth and range directions are perpendicular to each other).  35  The transmitted pulses travel at the speed of light (c = 3x10  m/s) which is much faster  than the velocity of the antenna. In this way, the antenna can be treated as stationary from the time when it sends out one pulse and receives the reflections from the ground targets. Then the antenna moves to one position to the next azimuth position, sending out another pulse and receiving the back scatter again. Because of the large disparity in time duration of the two directions, azimuth is referred to as the "slow time" axis while range as the "fast time " axis.  Figure 12 Synthetic aperture radar geometry  36  Let r\ represent the slow time variable in azimuth direction. Then at each r\ there is a fast time variable t corresponds to the signal in the slant range (R) direction. The transmitted pulse is a chirp signal, exp(y'7TK t ),  and the ideal received S A R signal from a point  2  r  target can be written as a two dimensional signal [3]: 2R(V)  s (t, r\) = P(t) A(r]) exd  A (36)  In (36) P(t) is the pulse envelope in range direction, A(r\) is the azimuth antenna pattern, K is the range F M rate, and X is the radar wave length. The received signal s(t,r]) can be r  separated to the range signal sft,rj) and the azimuth signal s (rj) as follows: a  s(t,rj) =  s (t,ri)xs (ri) r  a  (  s (t,rj) r  = P(t) exp]  jnK  t  v f  s (T]) = A(77)exp  2R(M ,2 \  r c  4R(r])^  a  (37) After the chirp travels through the slant range R(r\) and back, the received signal s has a r  time delay 2R(r])/c. In this case, for the same target, but in a different azimuth position, the time delay of the received chirp is different, causing range cell migration in the data memory.  37  From the geometry of Figure 12, the closest slant range of the target (R ) is at azimuth 0  time r\ = no. When the target is at some arbitrary position with respect to the antenna, the slant range R can be expressed as: R(n) =  JR? V?(n-n y >+  o  (38) Because Ro»  Vfn-rio), equation (37) can be approximated by a parabola, expanding the  equation in a second order Taylor series around r|o:  R(n) = R + 0  2R  n  (39) Combining equation (37) and (39) together, the received azimuth signal can be written as:  s (n) a  = A(r\) exp  V exp  4 77T  r  2V  ^  2  •i»7t(l-1o)  2  K A 0  = A(r\) exp  ]7l-  4R  n  X  exp(-jn  K (n-r} ) ) 2  a  0  (40) where the K is the azimuth F M rate. Note, that the value of Ro changes for each range a  cell, therefore the azimuth F M rate changes also, and this must be taken into account when processing the data from different range locations. A l s o note, that sjr])  has a  constant phase -4nRo/X proportional to Ro. This constant phase must be preserved or recovered after the azimuth compression. It is needed for further S A R processing applications, such as S A R interferometry (InSAR).  38  The time variable 77 in the azimuth signal is valid within the exposure period of the target, which is determined by the antenna pattern A(r]). When the antenna length is L , the foot print width of the antenna beam at the target is XRo/L, so the target exposure time is  _ AR  0  ~LV  e  r  (41) Let r\ represent the azimuth time when the beam center crosses the target. Then, r\ = c  c  Rotan(©/V ), r  and the valid interval of t] for s (r]) is: a  n ~  T  c  *  V  <  T  (42)  3.3  SAR signal compression  The received S A R data in both range and azimuth can be modeled as the convolution of a linear F M chirp and the ground reflectivity. Using the form of the ideal received signal in equation (36) a matched filter can be derived for each dimension and a pulse compression can be performed on the received data. The pulse compression rearranges the energy received from each ground targets into a single focused pulse. The location of the maximum energy of the pulse corresponds to the location of the target in range and azimuth, while the strength of the pulse represents the reflectivity of the target.  The Range/Doppler (RD) algorithm is a traditional, highly accurate and efficient method for compressing S A R data. It consist of the following major stages [3]:  39  •  range F F T ,  •  range matched filter multiplication,  •  range EFFT,  •  azimuth F F T ,  •  range cell migration correction ( R C M C ) ,  •  azimuth matched filter multiplication and  •  azimuth EFFT.  In the R D algorithm, the range and the azimuth signal are compressed separately using different matched filters. In either case, the matched filtering can be implemented via time domain convolution or frequency domain multiplication. In the rest of this section, the pulse compression is introduced through the frequency domain azimuth compression.  The azimuth signal in (40) can be simplified without loss of generality by ignoring the phase term exp(-JTV4RO/A) and the antenna pattern A(n): s (n) a  = exp(-jn  K (t]-rj ) ) 2  a  0  The spectrum of the signal in equation (43):  S (f) a  dc  = rect  exp  2f%  K.  (43) Where Fd is the Doppler centroid frequency and Fd c  bandwidth of the azimuth signal  c  = -K (n -r]o). a  c  The Doppler  BW=T K . e  a  The matched filter in the azimuth frequency (Doppler) domain is the complex conjugate ofS (f): a  40  M(D=s:(f\  o=0  (44) The frequency domain compressed signal is the product of the spectrum of the matched filter and the spectrum of the azimuth signal: S (f) c  =  M(f)-S(f)  exp(-;'27z:/77)  = red  0  (45) The compressed signal in the time domain is the inverse Fourier transform of S (f): c  S (TI) = F{SJf)} = K T x (j2nF (r] c  A  ee  P  dc  - )) Vo  sinc(^  T (r)-r),)) e  (46) In (48), the compressed peak is at the point of the target's closest approach (r\o). This compressed peak location can be changed to other position, such as the target's starting time or Doppler centroid location, by changing the format of the matched filter.  In the following two chapters, survey on the applicability of the Momentary Fourier Transform to S A R signal processing algorithms is given. Chapter 4 shows how the M F T can be applied to the S P E C A N S A R processing algorithm, while i n Chapter 5 it is shown what advantages the M F T offers when it is used for burst-mode data processing.  41  Chapter 4  A p p l i c a t i o n o f M F T to S P E C A N S A R P r o c e s s i n g Algorithm  4.1  Introduction  As it was mentioned in the previous chapter, S A R signal compression in range and azimuth can be accomplished by cross-correlation in the time domain using time domain convolution, or in the frequency domain, using the fast-convolution variant RangeDoppler method. Alternately, advantage can be made of the linear F M structure of signals by  replacing the  cross-correlation  operation  with a frequency  filtering  operation  performed by a D F T . This method is called SPECtral ANalysis ( S P E C A N ) [11]. In this chapter after the theory of the S P E C A N algorithm discussion on the application of M F T to azimuth S P E C A N processing is given.  4.2  The SPECAN Algorithm  The S P E C A N algorithm consists two major computational steps: •  Deramping  42  •  D F T extraction.  Deramping is the operation of multiplying a linear F M signal with a complex conjugate reference signal with the same F M rate, but opposite F M slope. The deramping of a signal containing multiple targets is shown in Figure 13. 1  2  3  4  5  6  7  7  8  9  10 11  12 13 14 15 16 17  F„ H z  (a) Frequency - time history of a set equally spaced point reflectors  Time  F„ H z  Time  (b) Frequency - time history of the reference function  15 14 13  F. H z  12  Time (c) Frequency • time history of the product function when the signal in (a) is multiplied by the reference function (b)  Figure 13 Deramping of multiple targets  43  After deramping, the targets whose zero Doppler frequency is located within one reference function cycle w i l l have frequencies ranging from -FJ2 to FJ2, where F is the a  azimuth sampling frequency. These targets w i l l constitute one processing region. The formation of parallelogram shaped regions from the deramped targets is shown in Figure 13 (c).  Note that each incremental step in the time direction in Figure 13 (a) results an incremental step in the frequency direction in (c), and that the frequency continuity is reset by F H z (i.e. / = 0 a n d / = F are connected). a  a  Consecutive processing regions are separated by lines of slope of the azimuth F M rate (K ) and constitute a parallelogram shaped area. Figure 14 shows one processing region a  in more detail. The parameters defined on the figure are calculated as follows: 2V  2  •  Azimuth F M rate: K = n  — [Hz/s] AR r  0  F M - —2  •  One cycle of the reference function:  •  The processed region of the total Doppler spectrum:  3  [samples]  M =(\.-/3)M, p  where  parameter j3 denotes the guard band. •  Velocity of a sub-satellite point: V [m/s]  •  Wavelength: A [m]  •  Closest slant range: Ro [m]  •  Sampling frequency of the azimuth signal: F [Hz]  r  a  44  Each deramped target in the parallelogram has a unique frequency ranging from -FJ2 to +FJ2. It is this unique frequency which defines the position of each target with respect to other targets in the same parallelogram.  •<  N = input D F T samples •  Time  : G good D F T output samples : discarded D F T output samples  Figure 14 Processing regions and the placement of successive D F T blocks i n single look case  The parts of the energy of any target which originate near the ends of each trajectory (i.e. near the sloped lines) has poor S N R because they arise from the low gain part of the azimuth beam profile and because of the relatively high presence of aliased energy there.  45  The guard band is represented by dashed lines in Figure 14. The total width of the guard band in the time direction is (3 times the distance between the sloped lines. This means that only (i-/3) fraction of the total available Doppler spectrum of each target trajectory is processed.  The next step in the processing is to separate the targets into different energy cells with regard to their position in the time domain. This is done by performing short length D F T s across the deramped data. The placement of the D F T s is also shown in Figure 13, where the D F T length is N samples. The location of the first D F T block is arbitrary, but for the sake of illustration it is placed so that the last sample corresponds to the bottom right corner of the processing region. The first D F T rectangle is divided into two parts by a horizontal line where the left-hand side of the rectangle touches the right side of the guard band. The upper section of the rectangle contains invalid output samples which must be discarded because the targets in that region are not fully exposed, while output points corresponding to the lower section of the rectangle are kept as the good D F T output points.  If N = ccM, then the unused portion of the available time axis is (l-a-(3) M samples long and the height of the valid part of the rectangle is G = (1-a-p) N  (28) D F T output samples. G is the number of good points retained from the D F T operation.  46  The next D F T must be applied so that the frequency of the lowest frequency cell corresponding to the good output region is exactly one cell higher than the highest frequency cell of the current D F T . This placement is shown i n Figure 14, as well. The gap between successive D F T s in the input time domain:  g = (l-2cc-P)M (29) The D F T length is governed by the desired azimuth resolution p , by the relation: a  0X9V F  N=  r  _0.89F A/g  a  a  0  (30) where cr is the weighting factor used in the application of D F T such that o~N is the effective number of samples used in the D F T input. F r o m (30) it is seen that the D F T length (AO is directly proportional to the range (Ro) and inversely proportional to the resolution (p ), while the variables V , F and K are defined by the S A R system. The a  r  a  a  azimuth F M rate strongly depends upon the range while V depends weekly upon R for r  satellite systems and constant for airborne systems. The azimuth resolution can be expressed from (30): _0.89y F r  ~  oNK  Pa  a  a  _0.89 ~  F XR a  0  oN2V  r  (31) Table 3 gives available azimuth resolutions for various D F T lengths, with the following C-band S A R satellite parameters: K  a  = 2100 Hz/s, F  a  = 1650 Hz, V = 6800 m/s, a = r  0.68.  47  D F T length - N  Resolution - p  256  23 m  512  11 m  a  Table 3 Resolution versus D F T length in S P E C A N for C-band satellite S A R  4.3  M u l t i look processing i n S P E C A N  The processing scheme of Figure 14 is a single look processing because the azimuth compression operation produces only one output point for each azimuth location. A multi look processing scheme is introduced as follows.  The large gaps between D F T s in Figure 14 shows that much of the input data is not used, which is indicative of the excess azimuth bandwidth available when this length of D F T is used. This excess bandwidth can be used to generate multiple looks.  Extra looks can be generated by dividing the G good output points from a single D F T into Ni equal groups, and assigning each group to a different look number. Ni is the number of looks. The next D F T is then placed so that the beginning of its good output points are contiguous with the end of the first look of the first D F T . Such a division into looks and placement of the second D F T is illustrated in Figure 15 for the four-look case. In this case the target is fully exposed in four consecutive D F T s , and the fifth D F T would have the same location as the second D F T in Figure 14.  48  Using the good points, obtained from each D F T output (28), the number of good output points per look is  L=INT  N, (32)  Time  Figure 15 Division of the good output samples into looks and the location of the D F T operations in multi-look processing  Note that G/Ni is not in general an integer so that usually LNi < G and that a small fraction of the good output points are not used. L must be an integer so that the looks fit together evenly at look summation time for each successive D F T .  49  The shift between successive D F T s is: LF  R  0  NK  a  A  F {l-P)  0.89  a  2 p  a  (33) In general q is not an integer, and the nearest integer must be chosen in order to decide where to place the second D F T .  For complete efficient data utilization q ~ o~N, so the number of looks that are normally taken can be expressed as follows:  I  V =_S_(l.^)-I = '  |  oNK  a  AP _(i. a  0.89V  '  / 5  )-l»^ . a  a  V  r  (34) Note that Ni is linearly proportional to p and does not depend on RQ. a  4.4 The SPECAN Algorithm Using the MFT The azimuth F M rate of the received S A R signal is inversely proportional to range, so it changes as the range varies in each cell. In order to keep the resolution and output sampling rate constant across the swath, there is a need to choose different D F T lengths which vary with one or a few samples at a time. The affect of the varying range on the F M rate and the required D F T length for spaceborne and airborne case are shown in Figure 16.  50  Azimuth F M Rate and DFT Length in SPECAN  i~ /  310  j§  /  / /  \  I  I  , *-D F T  :  /  t  /  / - •*  A dmuth FM Rate—»  i  r '. j  :  length  >w  r - '  302 300  / ~ /  830  835  840  845  850 Range [Km]  855  860  865  870  (a) Spaceborne S A R processing Azimuth F M Rate and DFT Length in SPECAN  ^-DFT lengt  I o  *•  300  I £  • *  •  Azimuth FM Rate^  12 14 Range [Km]  (b) Airborne S A R processing Figure 16 Azimuth F M rate and the D F T length with varying range  The radar parameters for the two cases are given, in Table 4. Note that in the spaceborne case there is a need for only 16 different D F T sizes to keep the resolution constant through the whole swath, while in the airborne case a wide range of window length is needed.  51  Radar parameter  Spaceborne SAR  Velocity (V' )  7000  Wavelength (A)  0.057  Weighting parameter (<T)  0.68  Guard hand ((I)  0.15  Slant range (R)  830 - 870  r  Sampling frequency (F,,)  Airborne SAR  [m/s] 0.057  0.15  a  Lkm|  300  [Hz|  HBilIsI  -  4  [m]  25  Azimuth resolution (p )  [m]  JlllllIMI  1680  Number of looks (A-'/)  Units  Table 4 Spaceborne and airborne S A R parameters for S P E C A N arithmetic calculation  The Radix-2 F F T algorithm can be only used when the D F T length is a power of two. In other cases of window length a mixed-radix F F T algorithm is used to achieve efficiency only for highly composite N. It means for each different D F T a different F F T algorithm should be  implemented  within  the  SPECAN  processor,  which makes the  DSP  architecture rather complex when many lengths of F F T are needed.  In contrast to F F T algorithms, the structure and the efficiency of the M F T does not rely upon on the size of the D F T . The same simple algorithm can be used to calculate all of the necessary D F T s during the azimuth compression. The number of real operations of M F T to process an M samples long region, when all of the spectrum coefficients are computed (N = AO is given in equation (32). c  52  During the D F T extraction only a portion of the spectrum coefficients - good output samples (G) - are used at the same time to obtain the compressed data. Although the amount of these spectrum components remains the same through the processing, but their position changes with the D F T s . So, the conventional form of the reduced-MFT algorithm cannot be used in this case. The sub-band of the calculated spectrum coefficients has to change its positions in the frequency domain in phase with position of the good output samples. The arithmetic of the required reduced-MFT algorithm is introduced below.  For the first M F T ( D F T 1 in Figure 13) all of the good output samples are computed for the first time so the arithmetic of the first M F T is: MFTOPS ,  =N [8 G + 2 ]  DFT  (35) For the next M F T ( D F T 2 in Figure 13) the position of the sub-band of the good output points shifted towards to the higher frequencies with L samples (the good output points in a look). Thus, there are L new frequency components to calculate beside the (Ni-L) continuously calculated ones. The number of real operations needed for the L new coefficients of the second D F T are: MFTOPS  DFT  2 NEW  =N [ 8 L + 2 ] (36)  The arithmetic of the previously computed spectrum coefficients of the second D F T is: MFTOPS  DFT20LD  =q [8(/V, - l ) L + 2 ]  53  (37) The shifting of the interval of the good output points in the frequency domain continues during the whole azimuth processing, which means that the 'new' and the 'old' spectrum R  coefficients have to be computed  M - N  ^  + 1 times through the whole region. Using the  previously introduced equations  R  MFTOPS  = MFTOPS  D F T  +  L  R  = N[SG  +  2] +  M - N  M - N  ^  +1  ^  +1  (MFTOPS  N[SL  DFT2NEW  +  2]+  (M-N  + MFTOPS  D  F  T  1  0  L  D  )  +1 4 [ 8 ( ^ - 1 ) ^ + 2 ]  (38) real operations are needed to process the whole processing region with the reduced M F T . Although equation (38) looks rather complex, the implementation of the above described reduced M F T algorithm is the same as the full M F T algorithm, except the timing and synchronization of the sub-band of the spectrum coefficients. Figure 17 shows the number of operations of the S P E C A N azimuth compression for spaceborne and airborne cases.  54  D F T Operations in S P E C A N  D F T Operations in S P E C A N  250  300 350 Window size [samples]  (b) Airborne S P E C A N processing  Figure 17 Arithmetic of S P E C A N azimuth compression with different D F T algorithms  The D F T algorithms used to obtain the D F T output samples are the direct D F T algorithm, the full and reduced M F T , the mixed-radix and radix-2 F F T . Although the radix-2 F F T algorithm is the most efficient, it can be used only once during the process, when the D F T length is 256 samples (Figure 17 (b)). A s the window length gets larger ( K smaller) a  55  the size of the processing region also gets larger, thus more operation is needed to process one range cell. The envelope of the plots in Figure 17 (a) and (b) represents the tendency of the growing arithmetic of S P E C A N .  Note that the arithmetic of the full and reduced M F T is more uniform in contrast with the arithmetic of the mixed-radix F F T algorithms. A l s o note, that the arithmetic o f the mixed-radix algorithm is equal to the direct D F T i f the window length is a non-composite number (i.e. prime). In Table 5 the ratio between the maximum and minimum of real operation of the reduced-MFT and mixed-radix F F T are given, followed by the ratio between the average operation of reduced and f u l l - M F T and mixed-radix F F T of the full swath.  Ratio of flops  Spaceborne SAR  NU v o l i c d i k v d Ml '1  11.15  M i n . of reduced M F T Max. of mixed radix F F T M i n . of mixed radix F F T  18.00  reduced M F T per swath  mixed radix F F T per swath  141.25  1.74  mixed radi \ F F T per swath full M F T per swath  Airborne SAR  1.42  2.17  Table 5 Reduced and full M F T versus mixed-radix F F T  56  From Table 5 we can see that in the spaceborne case there is less than 10 % difference between the minimum and maximum of the reduced-MFT flops, while the maximum of the real operations of the mixed-radix F F T more than 18 times bigger than the minimum. In the airborne case, the azimuth F M rate changes more dramatically, thus the difference between the minimum and maximum flops are much larger. When the M F T is applied to S P E C A N there is a factor of 11 difference between the maximum and minimum arithmetic, while in the case of the F F T the maximum of flops is 141 times bigger than the minimum. This large difference in the processing arithmetic makes the timing of the data flow in S P E C A N rather difficult when F F T algorithms are implemented. Note, the results in Table 5 are strongly depend on the azimuth resolution and on the width of the swath.  In the S P E C A N algorithm, the resolution is inversely related to the D F T length, thus larger D F T s are needed to obtain finer resolution. A s the transformation length is getting wider, the interval of the good output points w i l l shorten, therefore more D F T blocks with higher overlap ratio w i l l be needed to cover the processing region (Figure 14). In this case, the S P E C A N algorithm requires more computation to obtain the azimuth compression. Figure 18 and 19 illustrate the real operations per input samples of the traditional Range-Doppler (RD) algorithm and the S P E C A N algorithm with the M F T and radix-2 F F T algorithms for a spaceborne S A R system. The used system parameters for the analysis are the same as in Table 4, except the number of looks, Ni = 1 in this case.  57  Figure 18 The arithmetic of S P E C A N when the M F T is more efficient  Operations/Input samples vs. DFT length in SPECAN and RD  Operations/Input samples vs. DFT length in SPECAN and RD  1  Full M F T Reduced M F T Radix-2 F F T Range-Doppler  1  1  1  1  1  S 0.03 o  ! » 0-025;  i  :... - vC-SRECAN-wiuvRadiX" 2. FFT-  -  Range—Do ppicr  801)  900 1000 Window size [samples]  [)  900 1000 Window size [samples]  (b)  (a)  Figure 19 The arithmetic of the Range-Doppler algorithm and the S P E C A N algorithm using the M F T and F F T algorithms  A s it was shown in Section 2.4.2, the M F T algorithm gets more efficient as the Radix-2 F F T when the shift between successive D F T s are not bigger than five samples. Small amount of shift between the D F T s and the corresponding resolution are shown, in Figure  58  18 (a). The shift between D F T s is smaller than six samples only for large window lengths (1181, 1182, 1183, 1184, 1185 samples), close to the maximum (N  max  = (l-fi)M  = 1186).  Thus the M F T algorithms are more efficient than the Radix-2 F F T algorithm only for very fine resolutions. In Figure 18 (b) the real operations per input samples of the S P E C A N algorithm with the f u l l - M F T , reduced-MFT and radix-2 F F T are shown.  Figure 19 (a) illustrates the arithmetic of the R D algorithm and the S P E C A N with different  D F T implementations.  A s the D T F length gets larger, thus the  resolution gets finer (i.e. N > 700 samples, p  a  azimuth  < 10.85 m) the R D algorithm gets more  efficient, even if the Radix-2 F F T is applied in S P E C A N (Figure 19 (b)). Thus, when fine resolution is needed, the R D algorithm is used for S A R signal compression. F r o m Figure 18 (b) and 19 (b) it can be seen that the F F T algorithms are more efficient than the fullor reduced-MFT for all transformation lengths usually used in S P E C A N , thus the M F T does not improve significantly the computational efficiency of the S P E C A N algorithm.  Beside the complexity and computational efficiency, another important issue in the S P E C A N algorithm to keep the output sampling rate constant. In other words, targets which are T seconds apart in azimuth input time must appear T seconds apart in the output data. It was shown in [11] that the azimuth output sample rate is  ^ = ^ - H z  (40)  59  The output sampling rate strongly depends on the azimuth F M rate, so when K changes a  trough the swath, there is also a need for slowly varying D F T length to keep the output sampling rate constant. Figure 20 shows F , as the function of range, when M F T and ou  radix-2 F F T is applied in the S P E C A N algorithm for spaceborne and airborne systems. Note when the M F T algorithm is applied, the output sampling rate is more uniform for both cases. During the application of the radix-2 F F T only two transformation lengths 256 and 512 - can be used for the airborne case, while only one, N = 512 can be used for the spaceborne case. This is the reason of the large migration of the output sampling rate when the radix-2 F F T is applied to the S P E C A N .  Output Sampling Rate  :  !  Output Sampling Rate  Radixr2 FFT: N = 512 Radix-2FF'T: N -.256and 512 .  £65  a  <2  ,s  60  o. E  i 55 Br S  50  MFT:  835  8411  845  850 Range [Km]  855  (a) Spaceborne case  860  865  870  12 14 Range [Km]  (b) Airborne case  Figure 20 The output sampling rate of the S P E C A N algorithm  In this chapter, the applicability of the M F T to S P E C A N S A R processing algorithm has been investigated. Although, the M F T does not improve the computational efficiency of  60  the S P E C A N algorithm, it has the following advantages over the radix-2 and mixed-radix F F T when they are applied to S P E C A N : •  The M F T has consistent computing load as the D F T length changes.  •  It is easier to implement the M F T algorithm for variable window length. The architecture of a S P E C A N processor using M F T is less complex, because the same M F T algorithm can be used for the different window lengths.  •  The full radar resolution can be achieved, because the full Doppler spectrum of the targets can be used for the compression by using high-overlapped D F T s .  •  The output sampling rate of S P E C A N is more uniform.  61  Chapter 5 Application of MFT to Burst-mode SAR Data Processing  5.1 Introduction Burst-mode operation is used in S A R systems, such as R A D A R S A T , to image wide swaths, to save power or to save data link bandwidth. In this operational mode the received data has segmented frequency-time energy in its Doppler history. There are several algorithms for burst-mode data processing: one of them is the Short JLFFT (SIFFT) algorithm which was proposed by D r . Frank Wong at M D A [17]. In this chapter, after the introduction of the burst-mode data and the SLFFT algorithm properties, a survey on the efficiency of the SLFFT and the applicability of the M F T to the SLFFT algorithm is given.  5.2  Burst-mode SAR processing  Burst-mode is commonly used in S A R systems in S c a n S A R mode, where the beam is switched between two or more swaths to maximize the imaged swath width. A 2-beam S c a n S A R mode is illustrated Figure 21.  62  Figure 21 Burst-mode operation in 2-beam S c a n S A R case  In this operation mode, the radar beam scans through one sub-swath for a certain time interval, and switches to the next one. After scanning through the second sub-swath, the radar switches back to the first one to start the next burst cycle. The burst cycle has to be short enough to make sure each target is fully exposed in at least one burst.  The data from one sub-swath have to be processed separately from other sub-swaths, because the radar beam covers a different ground area in different sub-swaths. In the azimuth direction, the data is segmented into discrete bursts (shaded area in Figure 21), while in range the signal of any sub-swath is continuous, thus the data is not acquired in discrete range bursts.  63  16 fully exposed targets Frequency-time diagram of 16 targets Continuous signal  N S . ^ ^ ^ ^ ^  Burst signal  slope = KJHz/s]  Target 1  Target 16  o  IS  3 a. a. m — *.  5 " > O O)  Target 1 Target 2 Target 3 Target 4 Target 5 Target 6 Target 7 Target 8 Target 9 Target 10 Target 11 Target 12 Target 13 Target 14 Target 15 Target 16  burst 2 l _  burst 1  burst 3  burst 4  Frequency  Doppler history of 16 targets  Figure 22 Burst-mode processing of 16 fully exposed and evenly spaced targets in one range cell  5.3 Properties of fully exposed targets in burst-mode processing A typical 2-beam burst-mode data collection pattern is shown in Figure 22. Data from 16 evenly spaced, fully exposed targets in one range cell are shown, where the burst length is 20% of the aperture length. Dashed lines show the azimuth exposure time of each  64  target i f the S A R were operating in continuous mode, and the solid parts of each line show that part of each target actually exposed in burst mode. Note that the part of the target's exposure captured in burst mode varies with each target, which is illustrated in the frequency-time diagram of Figure 22. Each successive target is received at a lower Doppler frequency within a given burst, but is later captured at a higher frequency in the next burst, as long as it stays within the beam. The pulse repetition frequency (PRF or F ) and the aperture length in azimuth time (T ) a  a  are  connected through K , a  F =T K a  a  a  [Hz] (47)  T  a  in the time-domain consist of 5 burst lengths, so F in the frequency-domain also a  consists of 5 burst bandwidths (Figure 22).  The Doppler history of the 16 targets is also shown in Figure 22, where it is seen that it takes up to 2 bursts to cover all of them. The plot shows the distribution of target spectral energy that would appear i f an azimuth D F T were taken over the full 4 bursts, thus the D F T length was 4 bursts plus 4 gaps long in this case. Note that some targets appear in 2 full bursts (e.g. Target 6), some appear in 3 full bursts (e.g. Target 11), while others appear in two full and one partial burst. In this case, the average number of target exposures or bursts per aperture is 2.5. The number of bursts/aperture in S c a n S A R systems is typically between 1.5 and 3.  65  If single-look complex processing is to be done, then there is a choice of which bursts to use for each target. Normally, the target exposures closest to the Doppler centroid (Fd ) c  w i l l be selected, as shown by the heavier lines in the lower part of Figure 22. However, other bursts may be chosen, when the data is processed for I n S A R purposes.  First partially exposed target  Frequency-time diagram of fully and partially e x p o s e d targets  r£  a E  First partially exposed target  < slope = K, [Hz/s]  First fully exposed target  Last partially exposed target  a oco 3 a. a. in j? <u  First fully exposed target •  Last fully exposed target  exposed target burst 1  burst 2  burst 3  O ut  burst 4  Last fully exposed target  First partially exposed target  H  From targets previous to synthetic aperture  burst 1  burst 2  Last partially exposed target  From targets after synthetic aperture  burst 3  Frequency Doppler history of fully and partially e x p o s e d targets  Figure 23 Burst-mode processing of fully and partially exposed targets in one range cell  66  5.4 Properties of partially exposed targets in burst-mode processing In the previous section the properties of fully exposed targets of a synthetic aperture were introduced when they are processed in burst-mode. These are the valid targets of continuous-mode processing: they have complete frequency-time  history and  get  compressed using the full Doppler aperture (F ) matched-filter. a  Beside the fully captured targets, there are also partially exposed targets both at the leading edge and trailing edge of the azimuth D F T . These targets are incomplete, so they are discarded during continuous-mode processing. The frequency-time diagram and Doppler history of the partially exposed targets are shown in Figure 23, where the light gray region corresponds to signals from targets that begin previous to the start of the D F T and the dark gray region corresponds to signals from targets which end after the end of the D F T . Note that most of the partially exposed targets are also completely captured by one or two bursts, so they can be fully compressed using one burst width of their spectrum. In this way more targets with lower resolution can be fully compressed in the same synthetic aperture as in the continuous-mode for a given D F T length.  In Figure 23, both partially exposed regions in the Doppler history have a triangle shape in frequency-time space and they complete each other to a rectangular shape. Originally, the position of the light gray region is before the region of the fully exposed targets, corresponding to the position of targets to the synthetic aperture in azimuth time domain. The change in the position of the light gray region is caused by the wrap-around properties of the D F T .  67  First partially  Frequency  Figure 24 Effect of the circular convolution on the targets Doppler history  Figure 24 shows the original parallelogram region of the Doppler history and how the light gray region gets 'wrapped-around', when frequency-domain circular convolution is used instead of time-domain linear convolution. Note, targets in the parallelogram region get compressed into different output cells, while when the top triangle gets wrappedaround different targets can be compressed into the same cell depending on which Doppler sub-band is used. This property of the wrapped-around target Doppler history has to be taken under consideration, when selecting good targets during burst-mode signal compression using D F T s .  68  5.5  The S I F F T Algorithm  Most S A R processing algorithms are based on the fast convolution principle where a frequency-domain matched filter is used in the azimuth or Doppler frequency domain. When this method is applied to burst-mode data, the inter-burst gaps are filled with zeros and all the bursts are compressed at once using a full length matched filter followed by an IFFT. However, the compressed targets are then left with a burst-induced modulation.  The S I F F T algorithm differs from the conventional fast convolution algorithm in that short, overlapped IFFTs are taken after the matched filter multiply in the Doppler domain [ 2 2 ] . So, when one burst of a target is fully captured by the I F F T , little or no energy from adjacent bursts of the same target is present in the same I F F T . In this way, each I F F T compresses a group of targets without interference (modulation) from other bursts, and an accurate impulse response is obtained. The I F F T is acting like a band-pass filter to extract target energy from the segmented form of the targets' spectra. The filter is time varying in the sense that each successive I F F T is applied to a different frequency band.  To capture a target fully, the length of the I F F T must be at least as long as the length of the bandwidth of one burst. The bandwidth of one burst is N K BW  Hz  =  [Hz] F  BW  bin  =  N  b  K  "^  F  F  T  a  [frequency bins]  (48)  69  where  Nb  is the burst length and  is the length of the azimuth F F T in time samples.  NFFT  Then, the minimum LFFT length is  A W „  = BW  =  bin  [samples]  (49) The EFFT cannot be longer than the bandwidth of one burst plus one gap, so that a fullyexposed target is not contaminated by a partial exposure of the same target at a different frequency. In the 2-beam ScanSAR case, the length of the gap is often equal to the burst length, so the maximum length EFFT is =7BW  N J V  / F F T max  ^  u  y  v  =2 bin  N b K  "  ^  N  f  f  t  j-,2  (50) In practice, these length limits must be modified a little because of the spreading of target energy in the frequency domain, i.e. a guard band is used when locating the IFFTs. Note, NIFFT max  and  N IFFT min  are proportional to K  a  and  NFFT-  The effect of this property is  discussed in Section 5.6, where the arithmetic of the SEFFT is given. Usually, smaller than  NFFT,  NIFFT max  is  so less than the whole Doppler spectrum is used for azimuth  compression, which means that the output resolution of the SEFFT algorithm is smaller than the maximum available by a factor of  Locations of  NIFFT max  and  N IFFT min  NIFFT/NFFT.  to extract targets with the highest energy using the  closest burst spectrum to Fdc (thick lines) are shown in Figure 25. Targets, which are after or before the synthetic aperture and partially exposed, are noted as A 1 - A 2 4 and B 1 - B 2 4 , respectively.  70  F i g u r e 25 H o w m i n i m u m and m a x i m u m L D F T i s p l a c e d to c o m p r e s s groups o f targets f r o m each burst  It can be seen that IFFT Max I captures the c o m p l e t e e n e r g y o f a s i n g l e burst spectra o f Targets 1, 6-11, 16-A5 B 1 0 - B 1 5 and B 2 0 - 2 4 . F o r these targets, IFFT Max I does n o t  71  extract any energy from other bursts' spectra, so their impulse response is not corrupted by modulation. Similarly, IFFT Max 2 captures the complete energy of a single burst spectra of Targets 1-6, 11-16, A 5 - A 1 0 and B15-20 and between the two IFFTs, all the targets are correctly compressed. In order to form a continuous output image, the results of successive IFFTs are stitched together. If only bursts with the highest energy are used to compress targets, then each output target gets to a different output cell. Note, not all the partially exposed targets can be compressed with the best S N R and Targets A16-24 cannot be compressed at all, because none of the bursts covers them completely. A l s o note, targets before the full synthetic aperture (B10-B24) get compressed at last and their position in the output array is unique and correct.  5.5.1 Number of good output targets of a single IFFT It was shown in the previous section that each I F F T compress different groups of targets correctly. W e refer to these correct results as "good" output targets of one I F F T The General form of  GIFFT  (GIFFT)-  w i l l be derived as below.  The number of groups of good output targets of an I F F T in the 2-beam burst-mode processing case is N  N group  _  l  y  FFT  (51) Note,  N p gr0U  is equal to the number of complete bursts in a D F T . In our example in Figure  25, the D F T consists of 4 burst and 4 gaps, so there are 4 groups of good output targets.  72  A l s o note, that Target 1 and Targets B20-B24 are in the same group or burst ( but Target B20-B24 is wrapped-around by the circular convolution).  The shift q between two consecutive targets in the Doppler frequency domain is shown on the top in Figure 25 and K N q = — | — [ f r e q u e n c y bins] 2  F  ^  A  IFFT  (52) Note, q is proportional to K and N FFT, SO the shift varies with range and the length of a  the D F T . Note, also that although targets are more than one samples apart in the Doppler domain, they placed one cell apart in the output space.  The number of good output targets in a group depends on the length of the I F F T and shift between targets: - BW  N n k group  — ~  IFFT  , +  ""bin  "  1 1  (53) Note, when NIFFT = NIFFTMIN, then  G  g r 0  = i as it is shown in Figure 25.  up  Then, the number of good output targets from an EFFT can be expressed as: (T,„™ — IFFT  (j  •N  group  N  FFT  2N  h  group  (N  IFFT  BW )F BIN  A  N  IFFT  ^  KN A  FFT  (54)  73  5.5.2  Real data simulation of burst-mode processing  A real data experiment was done to verify the above described properties of the S I F F T algorithm. A burst-mode processor was created by combining the M D A / U B C d t S A R processor and our Matlab azimuth processing program.  In the experiment, the raw data ingestion, range compression and R C M C are done by the d t S A R processor. The R C M C - e d data, along with the required processing parameters, such as F , K and the Doppler centroid frequency are read into our Matlab program for a  a  azimuth processing. Before we can apply the burst-mode algorithm to the d t S A R output data, we have to generate the burst mode signal and correct the antenna pattern to avoid scalloping in the output. The antenna pattern correction is done by summing the azimuth F F T of each range cell, and polyfitting the summed data.  The burst mode data is emulated from S A R continuous-mode data by windowing the signals in the azimuth direction in the Matlab program. The parameters of the E R S - 1 data are shown in Table 6. The single look detected S A R image generated with the S I F F T algorithm is shown in Figure 26. Value  Unit  Sampling lrequv.'iK> i / i  1679.90  [Hz]  Doppler Centroid (F^ )  447.01  [Hz]  Processing parameter  c  Burst length (N,,)  Samples  Range cells  1024  Samples  Azimuth samples (TV/v /)  2048  Samples  Table 6 E R S - 1 parameters for real data simulation 74  o o  «  O un CO  o o CO  o in CSI  <  o o (M  o un i—  o o r-  injiunzv  Figure 26 S L C product of burst-mode simulation  Although,  the  synthetic  approximately l / 5  t h  aperture length  varies  with range,  the  burst  length  is  of the aperture through the whole processing region. During the  azimuth compression, N  IFFT  MIN was used for bursts close to the Doppler centroid, so the  burst-mode data is extracted with the best S N R . The resolution of the output image is l/5  t h  of the original continuous S L C , because maximum only l / 5  t h  of the targets' Doppler  spectrum can be used for signal compression.  Figure 27 The Doppler history of real burst-mode data  The Doppler history of one range cell of the real data was generated using the L M F T algorithm is shown in Figure 27. LMFTs, with size of NIFFT MIN were taken, at each frequency bin, and the result of the transforms were stored in a matrix. Figure 27 shows  76  the values of the matrix, where the brightness of the image corresponds to the magnitude of the output result. The bright points represents the good output answers, while the darker parts show the gaps in the frequency domain. Note, the energy leakage of the good output points in the figure. When the I M F T is applied to process a data record, it needs N samples to fully contain data. So, in the output space there is an ./V sample long ramp before the first valid result. Similarly, after the last valid result there is an N samples long ramp as the I M F T is sliding off from the data. These properties of the frequency analysis cause the leakage in the output space. Ideally Figure 27 would look like the Doppler history in Figure 23, thus it would have the same intervals of gaps and targets.  5.6 Efficiency of SIFFT using the IMFT and the IFFT algorithms To show the efficiency of the  rMFT vs. the  I F F T algorithm we use the parameters of the  alternating polarization ( A P ) mode of E S A Envisat satellite. Alternating polarization mode provides high resolution products (approximately 30m) in any of the seven swath located over a range of incidence angles spanning from 15 to 45 degrees with polarization changing from sub-aperture to sub-aperture within the synthetic aperture. Effectively, a 2-beam case S c a n S A R technique is used but without varying the sub-swath.  The velocity of the satellite V = 6700 m/s, the radar wavelength X = 0.0567 m and the r  azimuth F F T is 2048 and 4096 samples long during the efficiency evaluation. Other parameters of the seven swath are given in Table 7.  77  Swathes  Sampling frequency F [Hz] a  Burst/Gap length in [samples]  IS 1  Range [km]  825 - 864  •f "';':!'"""I.":! h !^h!-LJP4pNi^^^^^^^y|^^li:!i!!i!i:H:! :E ::  : E  :  ::  IS 2  1645  IS 3  2096  IS 4  1680  IS 5 IS 6  1698  196  843 - 891 887-934  218  929 - 990  HH9NHI  <>S3 - 1032  238  1027- 1087  IS 7  IOMI  Table 7 Envisat swath parameters  5.6.1 Effect of varying SAR parameters and SNR/efficiency tradeoffs As it was shown previously, the azimuth F M rate of the received S A R signal is inversely proportional to range, so it changes as the range varies i n each range cell. While the length of the bursts is constant in the azimuth time domain, the bandwidth of the bursts (49) varies with range because it is proportional to K . Table 8 shows the maximum and a  minimum value of the burst bandwidth of each swath i n FIz and i n frequency bins. A t close range the bandwidth has its maximum, while at far range has its minimum as it is shown in Figure 28 for swath IS1. In a swath, the minimum I F F T length (ND?FT min) should be at least as long as the maximum BWbin to compress all the targets i n each range cell correctly. Note, the lower and upper limit length of the IFFTs related to the burst bandwidth so they are different for each swath as well.  78  BWbin [frequency bins]  BW  Hz  [HZ] N F F T ==  Swathes  2048  MLN  M A X  MLN  M A X  IS 1  212  222  259  IS 2  212  221  208S/:-':  IS 4  N F F T -=  4096  MLN  M A X  271  ^517  542  2o4  279  527  557  219  203  214  4Mo  428  208  221  253  :~o  506  539  IS 5  204  214  201  211  402  422  IS 6  204  216  246  261  493  521  210  198  397  llo  •-IS 3  IS 7  Table 8 M i n i m u m and maximum burst bandwidth of the seven swathes  The S N R of the SLFFT algorithm depends on the ratio of the LFFT and the burst bandwidth [22]. The S N R is maximum when the LFFT length is equal to the burst bandwidth (NIFFT = NIFFT  min),  while the S N R is 3 dB lower when the LFFT window is one  burst plus one equal-sized gap long (NIFFT = NIFFT  max)-  So, in order to keep the S N R loss  below a certain value across the swath, there is a need to choose specific azimuth LFFT lengths. The change in S N R for a given LFFT length is:  dSNR = 10 log 1 0  J  IFFT  V  K  BW  binj  (55)  If we choose NIFFT as small as possible at near range and have it stay the same through the whole swath, the S N R w i l l decrease, as the burst bandwidth decreases with range.  79  Figure 28 shows how dSNR changes with range, in the case of IS1 swath and NFFT = 2048.  The decrease in S N R is zero at near range and rises to about 0.2 d B at far range.  Note, although BWbin in (54) depends on NFFT, the gradient and maximum of dSNR is the same for different azimuth F F T lengths.  Figure 28 Burst bandwidth and dSNR of IS1 swath  5.6.2  Arithmetic of the SIFFT algorithm using the IMFT and the IFFT algorithms  During the efficiency evaluation of the SLFFT algorithm, the full-LMFT, the reducedI M F T and the mixed-radix LFFT algorithms are considered. A formula of the arithmetic of each algorithm is introduced below.  It can be shown that the number of LFFTs to compress bursts with the highest energy is  80  NIFFT = 2  N  N  G  b  FFT  group  2N N q lFFT  b  N (N -BW +q) FFT  lFFT  bin  (56) We can see from (56) that NIFFT inversely proportional to  G u, sr0  P  thus i f more targets are  in a group less LFFTs are needed for data extraction. Than, the number of operations needed to compress all the targets using the LFFT algorithm is OPIFFT  = NIFFT • OPN  ,  FFT  (57) where OPNi  FFT  is the number of operations needed for one N sample long mixed-radix  LFFT. Note i f N is power of 2 than OPN,  FFT  = 5N  log (N). 2  A s we saw in section 1.4.2, in case of the full-LMFT algorithm OPIMFT  = M(8NIFFT  + 2)  real operations are needed to analyze an M-point complex data record. In the case of 2beam burst processing M = 3BWb , so the arithmetic of the full-LMFT algorithm is in  OPIMFT =3BW (%N FULL  BIN  IMFT  + 2)  (58)  During the LDFT extraction in the SLFFT algorithm, only a portion of the output target space - 'good' output targets (GIFFT) - is compressed correctly. Although the amount of the 'good' targets remains the same through the processing of a range cell, but their position changes with the position of the LDFTs. So, a simple reduced-LMFT algorithm cannot be used for the target extraction. The position of the computed Doppler - spectrum  81  coefficients has to change in phase with position of the not-compressed good output samples, and the Doppler-frequency coefficients of the already compressed targets do not have to be computed during the rest of the processing. The arithmetic of the required reduced-MFT algorithm is given below.  A t first, the reduced - I M F T algorithm has to be applied NMFT times to give the first valid compression result. This requires OPIMFT  =N  Reducedl  IMFT  (SN  +2)  IMFT  (59) real operation. When the first I M F T is done, GIFFT number of targets are compressed correctly, so in the next I M F T NReduced 1 = NMFT - GIFFT number of targets have to be compressed. Than, the I M F T window is shifted q times, a sample at a time, till it fully covers the next target in the group. N o w , G p  number of targets can be extracted  gr0U  correctly, so during the next q shift Nn duced - G  SROUP  e  targets need to be extracted. The  deduction of the spectrum coefficients from the previously reduced whole output target cell repeats (3BWbin - NiFFrVq times trough the processing region, until all the targets get compressed. It can be shown that the arithmetic of this procedure is:  -N  3BW OPIMFT  Reduced2  =2[3BW -N bin  ][4(N -G )-2G  IMFT  lMFT  IFFT  IFFT  (  *  ^L-l)  + l]  q  (60) Using the previously introduced equations  82  0P1MFT,  Reduced  = =  OPIMFT,  Reduced 1  N (SN,IMFT IMFT  +  OPIMFT,  Reduced 2  + 2) + IMFT IMFT  - l ) + l]  (61)  real operations are needed to compress all targets using the reduced-IMFT algorithm. Note that the arithmetic of the reduced-IMFT depends on GIFFT and G  &ROUP  targets  get extracted by an I M F T  ,  thus i f more  than less computation is needed. Note,  the  implementation of the reduced-IMFT is the same as the f u l l - I M F T algorithm, except the timing of the position of the output targets need to be compressed.  During the efficiency evaluation, we choose the I D F T lengths on the principles that we want: •  maximum S N R at near range,  •  minimum sampling rate at near range, and  •  the sampling rate constant with range,  and we set the forward azimuth F F T to be 2048 and 4096 samples long.  First, we make the I D F T as small as possible at near range (i.e. NIDFT = Max BWbin), and have it stay the same with range, even though the burst bandwidth decreases with increasing range. Thus there is only one I D F T window length to choose from (w = 1) for the I F F T or the I M F T algorithms.  83  Second, we consider the case where the I D F T is allowed to be up to 4 samples longer than the minimum (i.e. Max BWu  n  ^ NIDFT < Max BWbm + 4). This allows some  flexibility in choosing a favorable I F F T length from 5 different window sizes, at the expense of a small decrease in S N R . Table 9 shows the I D F T lengths and the maximum of the corresponding dSNR for the seven swathes of the Envisat.  N  F F T  = 2048  w = 1  Swathes  NFFT  = 4096  w = 1  w =5  w =5  NIDFT  Max dSNR  NIDFT  Max dSNR  NIDFT  Max dSNR  , NIDFT  Max dSNR  IS 1  271  0.20  275  0.26  542  0.20  546  0.24  IS 2  279  0.24  280  0.26  557  0.24  560  0.26  is ^  - 214  (»23  216  0.27  42S  0.23  • 432  0.27  IS 4  270  0.28  270  0.28  539  0.27  540  0.28  IS 5  211  0.21  215  0.29  422  0.21  425  0.24  IS 6  261  0.26  264  0.30  521  0.24  525  0.27  210  0.26  416  0.20  420  0.24  208  : :  Table 9 The length of the IDFTs and the corresponding d S N R  The burst bandwidth is direct proportional to N FT (48), so it changes with the same ratio F  as NFFT does. Note that i f the value of BWbin is bigger, than it is easier to find a highly composite number in its neighborhood, thus it is easier to pick a suitable window length for the I F F T algorithm. Also note in Table 9, that the change in dSNR when a more  84  suitable window is used for the LDFTs is less than 0.1 d B , thus the S N R decrease is practically negligible.  Figure 29 shows the number of real operations of the SLFFT algorithm when it is used to compress the data of IS1 swath. The LDFT algorithms used to obtain the azimuth compression are the mixed-radix LFFT and the full- and reduced-LMFT. In Figure 29 (a), we see that the I M F T algorithms are more efficient trough the whole swath when only the minimum window length can be used.  The LFFT window begins to cover the bandwidth of more than one target as BWbm decreasing with range, so there is a change in the value of G up and GIFFT when the LFFT gr0  window starts to fully cover two or more targets in the Doppler domain. The I M F T arithmetic decreasing slowly with rage and the arithmetic of the mixed-radix LFFT drops down to its half when G p doubles from 1 to 2. The LFFT arithmetic is constant on both gr0U  side of the down-step. Note that there is more than a factor of 10 difference between the arithmetic of I M F T and LFFT, because the window length is prime (271), so only the direct LDFT can be used to obtain the LFFT results.  85  IS1 Swath Number of IDFT windows to choose from = 5  IS1 Swath Number of IDFT windows to choose from = 1 3.5  IFFT full-IMFT reduced-IMFT  IFFT full-IMFT reduced-IMFT 2.5 B25 £.20 JlS  825  830  835  840  845 850 Range [km]  855  860  825  830  835  a)  840  845 850 Range [km]  855  860  b)  Figure 29 Arithmetic of the SLFFT algorithm when applied to the IS1 swath  In Figure 29 (b), we can see that the mixed-radix LFFT arithmetic dramatically drops down when the LFFT window length can be a composite number (275). The arithmetic of the I M F T algorithms did not change significantly and they are efficient only in a part of the swath, where only one target can be fully compressed in each group. The down-step of the LFFT arithmetic happens in closer range, because the LFFT window is larger, so it starts to fully cover two targets earlier. Note that the reduced-IMFT arithmetic also drops down when G p doubles, but this change is not significant compare to the change in the gr0U  LFFT arithmetic. Note, the arithmetic of the I M F T and LFFT algorithms of the other swaths is some what similar to the arithmetic of swath LSI.  In figure 30, the average millions of operations ( M O P S ) are shown for all the Envisat swathes when the azimuth F F T 2048 and 4096 samples long. The trend of the arithmetic of the full- and reduced-LMFT is similar in all cases, while the LFFT arithmetic is quite  86  variable, depending upon the composition of the window length. When  NFFT = 2048,  the  I M F T is more efficient for most of the swathes even i f there is an option to chose a suitable window length for the LFFT algorithm. When  NFFT  = 4096,  the I M F T is almost  always better than the LFFT i f only the smallest LDFT window can be used (Figure 30 (c)). When there is a possibility to chose a favorable LFFT length, the LFFT is more efficient for all the swathes except one. Note, there is a higher possibility to find a high composite number in the neighborhood of the smallest window size value and more group of good targets (51) can be extracted, when the azimuth F F T larger. So, the arithmetic of the LFFT algorithm decreases more dramatically, than in the case of smaller NFFT  length.  Number of IDFT windows to choose from = 1, N  Envisat AP mode burst length [samples]  a)  = 2048  Number of IDFT windows to choose from = 5, N  = 2048  Envisat A P mode burst length [samples]  b)  87  Number of I D F T windows to choose from = 1, N  = 4096  Number of I D F T windows to choose from = 5, N  Envisat AP mode burst length [samples]  = 4096  Envisat AP mode burst length [samples]  c)  d)  Figure 30 Arithmetic of the S I F F T when it is applied to Envisat A P burst mode operation  From the above given arithmetic survey we can see that the I M F T algorithm can improve the computational efficiency of the S I F F T algorithm when, •  the azimuth F F T is relatively small,  •  the maximum near range S N R is required and  •  the I D F T window length is a non-composite number.  Beside its efficiency, the I M F T has the following advantages when it is applied to the SIFFT algorithm: •  the I M F T has more consistent computing load as the burst bandwidth changes with range and  •  it is easier to implement the I M F T algorithm for different burst and NFFT lengths, because the same I M F T algorithm can be used for the different I D F T window lengths.  88  Chapter 6 Conclusions  6.1  Summary  The objective of this work has been to further develop the theory of the momentary Fourier transform, to examine its arithmetic and efficiency and to show what advantages it offers when it is applied to the S P E C A N S A R algorithm and the SLFFT burst-mode data processing algorithm.  The momentary matrix transform was introduced and it was shown when it takes the form of the D F T or the LDFT, the resulting M F T / L M F T have an efficient recursive computational structure. The spectrum coefficients of the M F T / L M F T can be calculated independently and only one complex multiplication and two complex additions are needed to update each spectrum component. This is a factor of log N improvement over 2  the radix-2 F F T algorithm if all incremental D F T results are needed. The efficiency of the M F T / L M F T do not rely upon the transform length being a power of two, in contrast to  89  standard FFT algorithms. The MFT/IMFT transformation pair can provide more efficient computation of the DFT when: •  DFTs are highly overlapped  •  only a few Fourier coefficients are needed  •  a specific, non-composite DFT length is needed,  and they can be useful in different applications of signal processing such as: •  on-line computations in real-time spectral analysis  •  on-line signal identification and detection  •  speech processing  •  radar and sonar processing  •  narrow-band filtering.  After the introduction of the SPECAN SAR processing algorithm, the applicability of the MFT to SPECAN has been investigated. Although, the MFT does not improve the computational efficiency of the SPECAN algorithm, it has the following advantages over the radix-2 and mixed-radix FFT when they are applied to SPECAN: •  The MFT has consistent computing load as the DFT length changes.  •  It is easier to implement the MFT algorithm for variable window length. The architecture of a SPECAN processor using MFT is less complex, because the same MFT algorithm can be used for the different window lengths.  •  The full radar resolution can be achieved, because the full Doppler spectrum of the targets can be used for the compression by using high-overlapped DFTs.  90  •  The output sampling rate of S P E C A N is more uniform.  In burst-mode S A R processing, the time-varying spectral properties of the azimuth received data requires that highly-overlapped inverse D F T s used at specific points in the spectral domain to obtain the azimuth compression. It was shown that the I M F T can be more efficient than the LFFT when it is applied to the SLFFT burst-mode data processing algorithm, when: •  the azimuth F F T is relatively small,  •  the maximum near range S N R is required and  •  the LDFT window length is a non-composite number.  6.2  Future work  The research of this thesis project raised the following topics for further study: •  Further develop the theory of the momentary matrix transform, and see what other discrete  transforms,  which  are  used in signal processing can be efficiently  implemented using the M M T . •  Investigate i f there is a closed recursive formula of the M M T for shifts greater than one, using higher order permutational matrixes.  •  Further examine the applicability of M F T to other S A R processing techniques, such as interferometry S A R (InSAR) processing. The M F T can improve the computational efficiency of the local frequency estimation of interferograms, because frequency estimates are needed at every sample point of the interferogram. In addition, there is a possibility of obtaining better resolution estimates of the local frequency using two  91  passes of M F T , in order to provide more  accurate estimates of  unwrapped  interferogram phase. Compare the accuracy, signal-to-noise ratio and computational efficiency of the S P E C A N and the S I F F T algorithm, when they are applied to burst-mode  data  processing. Investigate how efficiently and with what parameters the S I F F T algorithm could be used to process continuous-mode S A R data.  92  Bibliography  [1] A . Papoulis, Signal Analysis. M c G r a w - H i l l , 1977 [2] G . Strang, Linear Algebra and Its Applications.  Saunders College Publishing, Third  Edition, 1988 [3] J. Curlander and R . McDonough, Synthetic Aperture Processing.  Radar:  System and  Signal  Wiley, N e w York, 1991.  [4] J. G . Proakis and D . G . Manolakis, Digital Signal Processing.  Prentice H a l l , Third  Edition, 1996 [5] R. R. Bitmead and B . D . O. Anderson, "Adaptive frequency sampling filters," IEEE Trans. On Circuits and Systems, vol. C A S - 2 8 , pp. 524-534, June 1981. [6] J. Dudas, The Momentary  Fourier Transform. P h . D . thesis, Technical University of  Budapest, 1986. [7] H . L i l l y , "Efficient DFT-based model reduction for continuous systems", IEEE Trans, on Automatic Control, vol.36, pp. 1188-1193, Oct. 1991. [8] B . G . Sherlock and D . M . Monro, " M o v i n g discrete Fourier transform", Proceedings-F,  IEE  vol. 139, pp. 279-282, A u g . 1992.  [9] S. Albrecht, I. Cumming and J. Dudas, "The momentary Fourier transformation derived  from  International  recursive  matrix  transformations",  in Proceedings  Conference on Digital Signal Processing,  DSP'97,  of  the  13th  (Santorini, Greece)  vol. 1, pp. 337-340, July, 1997. [10] I. Cumming and J. R. Bennett, "Digital processing of S E A S A T S A R data", IEEE 1979  International  Conference  on  Acoustic,  Speech  and  Signal  Processing,  (Washington, D . C . , U S A ) , A p r i l 2-4, 1979.  93  [11] I. Cumming and J. L i m , "The design of a digital breadboard processor for E S A remote sensing satellite synthetic  aperture radar", technical  Dettwiler,  July 1981. Final report for E S A Contract N o .  Richmond, BC, Canada,  report,  MacDonald  3998/79/NL7HP(SC). [12] M . Sack, M . Ito, I. Cumming, "Application of Efficient Linear F M Matched Filtering Algorithms to S A R Processing", IEEE Proc-F,  vol.132, no. 1, pp. 45-57,  1985. [13] T. Ngo and C . M . Vigneron, "Project Report: U B C S Q L P v. 1.7.", technical  report  Radar Remote Sensing Group, Electrical and Computer Engineering, University of British Columbia, 1995. [14] H . Hobooti, "Radiometric Correction in R a n g e - S P E C A N S A R Processing", M.A.Sc. thesis, Electrical and Computer Engineering, University of British Columbia, A p r i l 1995. [15] Sandor Albrecht, "The Momentary Fourier Transform and Its Application to the S P E C A N S A R Processing Algorithm", technical  report SA-97-01,  Radar Remote  Sensing Group, Electrical and Computer Engineering, University of British Columbia, 1997. [16] S. Albrecht and I. Cumming, " Application of Momentary Fourier Transform to the S P E C A N S A R Processing Algorithm", in Proceedings Processing  Conference, EUSIPCO'98,  of the IX. European  (Rhodes, Greece) September 8-11, 1998.  [17] F. Wong, "Processing Envisat A P mode with Range Doppler algorithm", report, MacDonald  Signal  technical  Dettwiler, Richmond, BC, Canada, January 1996.  [18] I. Cumming Y . Guo and F. Wong, "Analysis and Precision Processing of Radarsat S c a n S A R Data", In Geomatics in the Era of Radarsat, GER'97,  (Ottawa, Canada),  M a y 25-30, 1997. [19] F. Wong, D . Stevens and I. Cumming, "Phase-Preserving Processing of S c a n S A R Data with Modified Range Doppler Algorithm", in Proceedings  of the  International 94  Geoscience  and Remote Sensing Symposium, IGARSS'97,  (Singapure), pp.725-727,  August 3-8, 1997. [20] I. Cumming Y . Guo and F . Wong, " A Comparison of Phase-Preserving Algorithms for Burst-mode S c a n S A R Data Processing", in Proceedings Geoscience  and Remote Sensing Symposium, IGARSS'97,  of the  International  (Singapure), pp.731-733,  August 3-8, 1997. [21] Y . Guo, "Precision Processing of Burst-Mode S c a n S A R Data", M.A.Sc.  thesis,  Electrical and Computer Engineering, University of British Columbia, October 1997. [22] I. Cumming Y . Guo and F . Wong, "Modifying the R D Algorithm for Burst-mode S A R Processing", in Proceedings Radar, EUSAR'98,  of the European Conference on Synthetic Aperture  (Friedrichshafen, Germany), pp.477-480, M a y 25-27, 1998.  [23] S. Albrecht and I. Cumming, " The Application of Momentary Fourier Transform to SEFFT S A R Processing ", in Proceedings of the IEEE-SP International Time-Frequency  Symposium on  and Time-Scale Analysis, (Pittsburgh, U S A ) October 7-9, 1998.  95  

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