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UBC Theses and Dissertations

Restoration of random motion degraded sonar images Tseng, David Tai Hee


The problem of sonar images degraded by wave-induced random ship motion and their restoration by filtering methods is investigated. The nature of the random motion is examined in detail, and a model is set up to describe its power spectrum in terms of the sea spectrum and the ship's receptance. A sonar measurement formula and its approximated form is derived. It is shown that the approximation represents a signal with additive coloured noise process. The signal is the measured seafloor profile and is approximated by a first-order Markov process. Several filters are proposed: Kalman Filter, Recursive Least Squares Interpolating (RLSI) Filter, and Adaptive ARMA Filter. In addition, Fast Estimation Algorithm and Adaptive Algorithm are introduced to determine unknown parameters in the Kalman Filter. Simulation results are generated using these filters. Performances are found to be strongly dependent on both signal and noise characteristics, with the exception of the RLSI Filter, which is relatively independent of wind speed, the main noise parameter. Computational complexities, estimation delay and convergence rates associated with the various filters are also examined. Finally, Extended Kalman Filter and Self-Tuning Filter are proposed as possible candidates for dealing with non-stationary, time-varying degradation problem.

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