Maritime Target Detection and Tracking Strategy Based on the Collaboration of Air-Sea-Submarine Cross-domain Unmanned Platform

This paper proposes a maritime target detection and tracking strategy based on the collaboration of air-sea-submarine cross-domain unmanned platform. Firstly, a maritime cross-domain unmanned system that integrates unmanned aerial vehicle (UAV), unmanned surface vessel (USV), and autonomous underwater vehicle (AUV) is constructed. Then, the optimal detection formation of UAV-USV-AUV is analyzed using measure theory to maximize target detection probability, accounting for the high maneuverability of maritime targets and the constraints of unmanned platform; After the target is detected, an inverse reinforcement learning-based controller is designed for the formation of UAV-USV-AUV to achieve reliable and effective tracking of surface/underwater targets in obstacle-prone environments. Finally, simulations and experiments are conducted to validate the effectiveness of the proposed method. The results show that the proposed detection mode can maximize the probability of detecting mobile targets within a finite time, and the proposed inverse reinforcement learning formation controller can achieve secure and collaborative tracking of cross-domain unmanned platform in complex environments by combining dynamic obstacle avoidance strategies while maintaining formation stability.