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BiLSTM-Attention-PFTBD : Robust Long-Baseline Acoustic Localization for Autonomous Underwater Vehicles in Adversarial Environments Jia, Yizhuo; Lou, Yi; Zhao, Yunjiang; Sun, Sibo; Cheng, Julian
Abstract
The accurate and reliable localization and tracking of Autonomous Underwater Vehicles (AUVs) are essential for the success of various underwater missions, such as environmental monitoring, subsea resource exploration, and military operations. long-baseline acoustic localization (LBL) is a fundamental technique for underwater positioning, but it faces significant challenges in adversarial environments. These challenges include abrupt target maneuvers and intentional signal interference, both of which degrade the performance of traditional localization algorithms. Although particle filter-based Track-Before-Detect (PFTBD) algorithms are effective under normal submarine conditions, they struggle to maintain accuracy in adversarial environments due to their dependence on conventional likelihood calculations. To address this, we propose the BiLSTM-Attention-PFTBD algorithm, which enhances the traditional PFTBD framework by integrating bidirectional Long Short-Term Memory (BiLSTM) networks with multi-head attention mechanisms. This combination enables better feature extraction and adaptation for localizing AUVs in adversarial underwater environments. Simulation results demonstrate that the proposed method outperforms traditional PFTBD algorithms, significantly reducing localization errors and maintaining robust tracking accuracy in adversarial settings.
Item Metadata
| Title |
BiLSTM-Attention-PFTBD : Robust Long-Baseline Acoustic Localization for Autonomous Underwater Vehicles in Adversarial Environments
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| Creator | |
| Publisher |
Multidisciplinary Digital Publishing Institute
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| Date Issued |
2025-03-12
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| Description |
The accurate and reliable localization and tracking of Autonomous Underwater Vehicles (AUVs) are essential for the success of various underwater missions, such as environmental monitoring, subsea resource exploration, and military operations. long-baseline acoustic localization (LBL) is a fundamental technique for underwater positioning, but it faces significant challenges in adversarial environments. These challenges include abrupt target maneuvers and intentional signal interference, both of which degrade the performance of traditional localization algorithms. Although particle filter-based Track-Before-Detect (PFTBD) algorithms are effective under normal submarine conditions, they struggle to maintain accuracy in adversarial environments due to their dependence on conventional likelihood calculations. To address this, we propose the BiLSTM-Attention-PFTBD algorithm, which enhances the traditional PFTBD framework by integrating bidirectional Long Short-Term Memory (BiLSTM) networks with multi-head attention mechanisms. This combination enables better feature extraction and adaptation for localizing AUVs in adversarial underwater environments. Simulation results demonstrate that the proposed method outperforms traditional PFTBD algorithms, significantly reducing localization errors and maintaining robust tracking accuracy in adversarial settings.
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| Subject | |
| Genre | |
| Type | |
| Language |
eng
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| Date Available |
2025-05-05
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| Provider |
Vancouver : University of British Columbia Library
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| Rights |
CC BY 4.0
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| DOI |
10.14288/1.0448750
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| URI | |
| Affiliation | |
| Citation |
Drones 9 (3): 204 (2025)
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| Publisher DOI |
10.3390/drones9030204
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| Peer Review Status |
Reviewed
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| Scholarly Level |
Faculty; Researcher
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| Rights URI | |
| Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
CC BY 4.0