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基于空−海−潜跨域无人平台协同的海上目标探测追踪策略

田泽兴 闫敬 高麒媛 杨晛 关新平

自动化学报2026,Vol.52Issue(2):349-362,14.
自动化学报2026,Vol.52Issue(2):349-362,14.DOI:10.16383/j.aas.c250438

基于空−海−潜跨域无人平台协同的海上目标探测追踪策略

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

田泽兴 1闫敬 1高麒媛 1杨晛 1关新平2

作者信息

  • 1. 燕山大学电气工程学院 秦皇岛 066004||智能控制与神经信息处理教育部重点实验室 秦皇岛 066004
  • 2. 上海交通大学自动化与感知学院 上海 200240
  • 折叠

摘要

Abstract

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 integ-rates 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 max-imize 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 mo-bile 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 dy-namic obstacle avoidance strategies while maintaining formation stability.

关键词

探测/追踪/跨域无人平台/避障/逆强化学习

Key words

detection/tracking/cross-domain unmanned platform/obstacle avoidance/inverse reinforcement learn-ing

引用本文复制引用

田泽兴,闫敬,高麒媛,杨晛,关新平..基于空−海−潜跨域无人平台协同的海上目标探测追踪策略[J].自动化学报,2026,52(2):349-362,14.

基金项目

国家自然科学基金(62222314,U25A20472),河北省燕赵青年科学家项目(F2024203047),河北省自然科学基金(F2022203001,F2024203072,F2025501051),河北省教育厅基金(JCZX2025027)资助Supported by National Natural Science Foundation of China(62222314,U25A20472),Yanzhao Young Scientist Project of Hebei Province(F2024203047),Natural Science Foundation of Hebei Province(F2022203001,F2024203072,F2025501051),and Education Department Foundation of Hebei Province(JCZX2025027) (62222314,U25A20472)

自动化学报

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