BiLSTM-Attention-PFTBD: Robust Long-Baseline Acoustic Localization for Autonomous Underwater Vehicles in Adversarial Environments
Abstract
:1. Introduction
- Enhanced localization accuracy through the integration of direct signals and comprehensive feature extraction: The proposed method utilizes both raw measurements and derived features from particle filtering. The system integrates normalized likelihood computation and peak detection analysis to enhance system robustness in adverse conditions. For detailed explanations, see Section 5.1.
- Advanced temporal modeling with BiLSTM for the detection of abrupt maneuvers and signal losses: Conventional tracking systems often fail with complex temporal dynamics and dispersed information. Our BiLSTM architecture processes sequences bidirectionally, enabling the effective prediction of abrupt maneuvers and signal losses in adversarial environments.
- Capturing complex dependencies with multi-head attention mechanisms: Traditional models struggle with dispersed temporal dependencies and complex data interactions. Our multi-head attention mechanism examines multiple sequence positions simultaneously, enabling dynamic focus adjustment and effective pattern recognition in challenging environments.
2. Particle Filtering Localization
2.1. Particle Filtering for State Estimation
2.2. Particle Initialization
2.3. Prediction Step for State
2.4. Update Step for Particle Weight
2.5. Resampling Implementation
2.6. State Estimation
3. Limitations of Traditional Particle Filters in Adversarial Environments
3.1. Mathematical Formulation of the Problem
3.2. Impact of Abrupt Maneuvers
3.3. Effect of Signal Jamming
3.4. Severity of Adversarial Conditions
4. Deep Learning to Enhance Particle Filter Performance
4.1. LSTM Cell Structure
4.2. BiLSTM Network Structure
4.3. Attention Mechanism
4.4. Multi-Head Attention Mechanism
5. Our Approach
5.1. Signal Features for Network Input
5.1.1. Estimated Position and Velocity
5.1.2. Position and Velocity Variance
5.1.3. Signal Peak Information
5.1.4. Likelihood Value Feature Extraction for Each Hydrophone
5.2. BiLSTM-Attention for Error Prediction
5.3. Error Correction with Network
6. Simulation and Results
6.1. Simulation and Experimental Setup
6.2. Dataset Construction
6.2.1. Trajectory Data
6.2.2. Feature Extraction at Each Time Step
6.2.3. Normalization and Dataset Preparation
- Input: A sequence of time steps, where each step contains a feature vector that includes information such as position, velocity, acceleration, and likelihood values.
- Output: The predicted error in the x and y coordinates at the corresponding time step allows the network to learn temporal dependencies and predict localization errors over time.
6.3. Experimental Evaluation Indicators
6.4. Result
6.4.1. Performance Comparison
6.4.2. Training Loss and RMSE
6.4.3. Position Estimation Error
7. Conclusions and Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
DURC Statement
Conflicts of Interest
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Parameter | Value |
---|---|
Simulation Area | 1000 m × 1000 m |
Localization System | Long Baseline (LBL) |
Buoy Positions | [(0, 0, −5), (1000, 0, −5), |
(1000, 1000, −5), (0, 1000, −5)] | |
Water Depth | 200 m |
Sound Speed | 1520 m/s |
Carrier Frequency | 37.5 kHz |
Number of Particles | 2500 |
Max Range () | 20 m |
Max Velocity () | 20 m/s |
Signal-to-Noise Ratio (SNR) | −12 dB |
Pulse Repetition Frequency (PRF) | 1 Hz |
Pulse Repetition Time (PRT) | 1 s |
Pulse Width | 10 ms |
Pulse Bandwidth | 100 Hz |
Sampling Rate () | 2 kHz |
Waveform | Rectangular Pulse |
Bottom Loss | 10 dB |
Number of Paths | 10 |
Type | Description |
---|---|
1 | Lateral movement |
2 | Vertical movement |
3 | Diagonal movement (combining lateral and vertical speeds) |
4 | Curved movement with lateral acceleration |
5 | Curved movement with vertical acceleration |
6 | Complex curved movement with both lateral and vertical acceleration |
Metric | BiLSTM-Attention | BiLSTM | LSTM | ||||||
---|---|---|---|---|---|---|---|---|---|
Training | Validation | Test | Training | Validation | Test | Training | Validation | Test | |
MAE | 0.72368 | 0.88564 | 0.90614 | 1.0043 | 1.4121 | 1.4079 | 1.419 | 1.9544 | 1.919 |
MAPE | 2.8529 | 4.2637 | 2.1524 | 3.8393 | 7.1145 | 3.0427 | 4.6777 | 8.5696 | 4.1049 |
MSE | 0.90529 | 1.439 | 1.5599 | 1.7915 | 3.8673 | 3.7198 | 3.7154 | 7.6711 | 7.211 |
RMSE | 0.95147 | 1.1996 | 1.249 | 1.3385 | 1.9666 | 1.9287 | 1.9275 | 2.7697 | 2.6853 |
0.98869 | 0.98145 | 0.98298 | 0.97657 | 0.94615 | 0.95219 | 0.9507 | 0.89159 | 0.90554 |
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Share and Cite
Jia, Y.; Lou, Y.; Zhao, Y.; Sun, S.; Cheng, J. BiLSTM-Attention-PFTBD: Robust Long-Baseline Acoustic Localization for Autonomous Underwater Vehicles in Adversarial Environments. Drones 2025, 9, 204. https://doi.org/10.3390/drones9030204
Jia Y, Lou Y, Zhao Y, Sun S, Cheng J. BiLSTM-Attention-PFTBD: Robust Long-Baseline Acoustic Localization for Autonomous Underwater Vehicles in Adversarial Environments. Drones. 2025; 9(3):204. https://doi.org/10.3390/drones9030204
Chicago/Turabian StyleJia, Yizhuo, Yi Lou, Yunjiang Zhao, Sibo Sun, and Julian Cheng. 2025. "BiLSTM-Attention-PFTBD: Robust Long-Baseline Acoustic Localization for Autonomous Underwater Vehicles in Adversarial Environments" Drones 9, no. 3: 204. https://doi.org/10.3390/drones9030204
APA StyleJia, Y., Lou, Y., Zhao, Y., Sun, S., & Cheng, J. (2025). BiLSTM-Attention-PFTBD: Robust Long-Baseline Acoustic Localization for Autonomous Underwater Vehicles in Adversarial Environments. Drones, 9(3), 204. https://doi.org/10.3390/drones9030204