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; 杨睍; 关新平

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

doi: 10.16383/j.aas.c250438 cstr: 32138.14.j.aas.c250438

田泽兴^{1, 2,},
闫敬^{1, 2,},
高麒媛^{1, 2,},
杨睍^{1, 2,},
关新平^3,

1.
燕山大学电气工程学院秦皇岛 066004
2.
智能控制与神经信息处理教育部重点实验室秦皇岛 066004
3.
上海交通大学自动化与感知学院上海 200240

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(2024203047), 河北省自然科学基金(F2022203001, F2024203072, F2025501051), 河北省教育厅基金(JCZX2025027) 资助

详细信息

作者简介:
田泽兴：燕山大学电气工程学院博士研究生. 2018年获得郑州大学自动化专业学士学位. 主要研究方向为刚性拓扑优化, 水下编队控制. E-mail: zxtian@stumail.ysu.edu.cn

闫敬：燕山大学电气工程学院教授. 2014年获得燕山大学控制科学与工程博士学位. 主要研究方向为水声传感器网络, 水下机器人控制. 本文通信作者. E-mail: jyan@ysu.edu.cn

高麒媛：燕山大学电气工程学院硕士研究生, 2023年获得燕山大学自动化专业学士学位, 主要研究方向为海空跨域平台目标追踪控制. E-mail: gaoqiyuan@stumail.ysu.edu.cn

杨睍：燕山大学电气工程学院教授, 2016年获得燕山大学控制科学与工程博士学位, 主要研究方向为网络化遥操作系统, 水下网络物理系统和非线性控制. E-mail: xyang@ysu.edu.cn

关新平：上海交通大学自动化与感知学院教授. 1999年获得哈尔滨工业大学控制科学与工程博士学位. 主要研究方向为工业信息物理系统, 无线组网及应用, 水下传感器网络. E-mail: xpguan@sjtu.edu.cn

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出版历程
- 收稿日期: 2025-08-31
- 录用日期: 2025-11-06
- 网络出版日期: 2025-12-03

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

TIAN Ze-Xing^{1, 2
,},
YAN Jing^{1, 2
,},
GAO Qi-Yuan^{1, 2
,},
YANG Xian^{1, 2
,},
GUAN Xin-Ping^3
,

1.
School of Electrical Engineering, Yanshan University, Qin-huangdao 066004
2.
Key Laboratory of Intelligent Control and Neural Information Processing, Ministry of Education, Yanshan University, Qinhuangdao 066004
3.
School of Automation and Intelligent Sensing, Shanghai 200240

Funds: Supported by National Natural Science Foundation of China (62222314, U25A20472), YanZhao Young Scientist Project of Hebei Province (F2024203047), National Natural Science Foundation of Hebei Province (F2022203001, F2024203072, F2025501051), and Education Department Foundation of Hebei Province (JCZX2025027)

More Information

Author Bio:
TIAN Ze-Xing Ph.D. candidate at the School of Electrical Engineering, Yanshan University. He received his bachelor degree in Automation from Zhengzhou University in 2018. His research interests include rigid topology optimization and underwater formation control

YAN Jing Professor at the School of Electrical Engineering, Yanshan University. He received his Ph.D. degree in Control Theory and Control Engineering from Yanshan University in 2014. His research interests include underwater acoustic sensor networks and the control of underwater vehicles. Corresponding author of this paper

GAO Qi-Yuan Master student at the School of Electrical Engineering, Yanshan University. She received her bachelor degree in Automation from Yanshan University in 2023. Her main research interest is target tracking control for sea-air cross-domain platforms

YANG Xian Professor at the School of Electrical Engineering, Yanshan University. She received her Ph.D. degree in Control Science and Engineering from Yanshan University in 2016. Her research interests include networked teleoperation systems, underwater cyber physical systems, and nonlinear control

GUAN Xin-Ping Professor at the School of Automation and Intelligent Sensing, Shanghai Jiaotong University. He received his Ph.D degree in Control Theory and Control Engineering from Harbin Institute of Technology in 1999. His research interests include industrial cyber-physical systems, wireless networking and applications, and underwater sensor networks

摘要

摘要: 提出一种基于空−海−潜跨域无人平台协同的海上目标探测追踪策略. 首先, 构建无人机−水面艇−潜器协同的海上跨域无人系统; 进一步, 针对海上目标的高机动性以及无人平台自身约束, 采用测度理论解析无人机−水面艇−潜器最佳探测编队队形, 实现目标探测概率最大化; 当探测到目标后, 设计基于逆强化学习的无人机−水面艇−潜器编队控制器, 实现障碍物环境下水面/水下目标的可靠有效追踪. 最后, 通过仿真与实验验证了所提方法的有效性. 结果表明, 所提探测模式可以实现有限时间内移动目标探测概率最大化, 同时所提逆强化学习编队控制器可以在保持队形稳定的基础上, 结合动态避障策略, 实现复杂环境下跨域无人平台安全协同追踪.
- 探测 /
- 追踪 /
- 跨域无人平台 /
- 避障 /
- 逆强化学习
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 integrates unmanned aerial vehicle (UAV), unmanned surface vessel (USV), and autonomous underwater vehicle (AUV) is constructed. Then, a detection formation of UAV-USV-AUV is determined using measure theory to maximize detection probability, accounting for target maneuverability and platform constraints. After the target is detected, an inverse reinforcement learning (IRL)-based controller is proposed 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 our detection mode can maximize the probability of detecting mobile targets within a finite time, and the proposed formation controller can achieve secure and collaborative tracking in complex environment by combining dynamic obstacle avoidance strategies while maintaining formation stability.
- detection /
- tracking /
- cross-domain unmanned platform /
- obstacle avoidance /
- inverse reinforcement learning

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图 1 无人机–水面艇–潜器协同探测追踪

Fig. 1 Collaborative detection and tracking of UAV-USV-AUV

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图 2 线集、带集与凸集的描述

Fig. 2 Description of line set, strip set, and convex set

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图 3 无人机–水面艇目标探测模式

Fig. 3 Target detection mode of UAV-USV

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图 4 水面艇–潜器水下目标探测模式

Fig. 4 Underwater target detection mode of USV-AUV

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图 5 基于逆强化学习的编队追踪控制器

Fig. 5 IRL-based formation tracking controller

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图 6 应对模型不确定性与环境变化的策略

Fig. 6 Strategy to handle model uncertainty and environment changes

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图 7 无人机–水面艇对水面目标探测结果

Fig. 7 Detection results of surface targets by UAV-USV

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图 8 水面艇–潜器对水下目标探测

Fig. 8 Detection results of underwater targets by USV-AUV

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图 9 基于逆强化学习的编队追踪控制器

Fig. 9 IRL-based formation tracking controller

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图 10 无人机–水面艇–潜器目标探测模式对比

Fig. 10 Comparison of target detection modes with UAV-USV-AUV

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图 11 无人机–水面艇–潜器编队追踪控制器对比

Fig. 11 Comparison of tracking controllers for formation of UAV-USV-AUV

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图 12 湖中实验设置

Fig. 12 Experimental setup in the lake

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图 13 湖中实验结果

Fig. 13 Experimental results in the lake

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图 14 湖中对比实验结果

Fig. 14 Comparison experiment results in the lake

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