Enhancing feature-aided data association tracking in passive sonar arrays: An advanced Siamese network approach

Feature-aided tracking integrates supplementary features into traditional methods and improves the accuracy of data association methods that rely solely on kinematic measurements. However, previous applications of feature-aided data association methods in multi-target tracking of passive sonar arrays directly utilized raw features for likelihood calculations, causing performance degradation in complex marine scenarios with low signal-to-noise ratio and close-proximity trajectories. Inspired by the successful application of deep learning, this study proposes BiChannel-SiamDinoNet, an advanced network derived from the Siamese network and integrated into the joint probability data association framework to calculate feature measurement likelihood. This method forms an embedding space through the feature structure of acoustic targets, bringing similar targets closer together. This makes the system more robust to variations, capable of capturing complex relationships between measurements and targets and effectively discriminating discrepancies between them. Additionally, this study refines the network's feature extraction module to address underwater acoustic signals' unique line spectrum and implement the knowledge distillation training method to improve the network's capability to assess consistency between features through local representations. The performance of the proposed method is assessed through simulation analysis and marine experiments.