Particle Filter-Guided Online Neural Networks for Multi-Target Bearing-Only Tracking in Passive Sonar Systems

This study proposes a novel method to address the instability issues in multi-target bearing-only tracking for passive sonar systems. Utilizing a particle filter-guided on-site training mechanism, the complex multi-classification task is simplified into binary classification (target vs. non-target) by assigning an independent tracker to each target. This enables simultaneous on-site training and deployment of the neural network for tracking. A hybrid CNN-BiLSTM network is constructed: the Convolutional Neural Network (CNN) enhances target feature extraction and non-target discrimination, while the Bidirectional Long Short-Term Memory (BiLSTM) models spatiotemporal dependencies. Their synergy improves trajectory continuity and smoothness. Under simulated conditions, the proposed method reduces the minimum required SNR for stable tracking to −31.78 dB, a significant improvement over the −29.69 dB required by pure particle filtering methods. The average tracking error is also reduced from 0.61° to 0.34°. Both simulations and sea trial data demonstrate that the method maintains stable tracking even during target trajectory crossings, significantly enhancing multi-target tracking accuracy in complex underwater acoustic environments.