AKansformer: Axial Kansformer–Based UUV Noncooperative Target Tracking Approach

Abstract

Uncrewed underwater vehicles (UUVs) are usually deployed to track noncooperative targets in industrial or military applications. However, the sonar measurement data usually show high unreliability due to the particularity of underwater environments. We propose a novel AKansformer Target Tracking (AKTT) approach to improve the accuracy and robustness of UUV tracking of noncooperative targets under unreliable measurement. First, a historical measurements–based non-Markov state-space model is constructed to capture the complexity of target dynamics under unreliable measurement conditions. Second, we develop a multistep prediction network that integrates the axial attention mechanism with Kolmogorov–Arnold networks (KANs) based on the sonar measurement model and target state transition model. The prediction network learns from massive offline data and predicts the regular transition of the target state, which provides a powerful prediction ability for the tracking approach. In view of the unreliability of the sonar measurement and the uncertainty of the posterior distribution, this article uses the Monte Carlo principle and the proposed multistep prediction network to transform the non-Markov system model into a recursive first-order Markov model and constructs the corresponding recursive filtering model. Finally, we conduct a comprehensive evaluation of the AKTT through an array of statistical experiments and simulation cases. The results validate the efficacy of the AKTT, showcasing its robustness and superiority in tracking noncooperative targets under unreliable, even intermittently failing, sonar measurements.