With the rapid development of underwater communication, sensing, automation, robot technologies, autonomous underwater vehicle (AUV) swarms are gradually becoming popular and have been widely promoted in ocean exploration and underwater tracking or surveillance, etc. However, the complex underwater environment poses significant challenges for AUV swarm-based accurate tracking for the underwater moving targets.In this paper, we aim at proposing a multi-AUV cooperative underwater multi-target tracking algorithm especially when the real underwater factors are taken into account.We first give normally modelling approach for the underwater sonar-based detection and the ocean current interference on the target tracking process.Then, we regard the AUV swarm as a underwater ad-hoc network and propose a novel Multi-Agent Reinforcement Learning (MARL) architecture towards the AUV swarm based on Software-Defined Networking (SDN).It enhances the flexibility and scalability of the AUV swarm through centralized management and distributed operations.Based on the proposed MARL architecture, we propose the "dynamic-attention switching" and "dynamic-resampling switching" mechanisms, to enhance the efficiency and accuracy of AUV swarm cooperation during task execution.Finally, based on a proposed AUV classification method, we propose an efficient cooperative tracking algorithm called ASMA.Evaluation results demonstrate that our proposed tracking algorithm can perform precise underwater multi-target tracking, comparing with many of recent research products in terms of convergence speed and tracking accuracy.

翻译：随着水下通信、传感、自动化及机器人技术的快速发展，自主水下航行器（AUV）集群在海洋探测、水下跟踪与监测等领域逐步普及并得到广泛推广。然而，复杂的水下环境给基于AUV集群的水下运动目标精确跟踪带来了巨大挑战。本文在综合考虑实际水下因素的前提下，提出了一种多AUV协同水下多目标跟踪算法。首先，我们建立了基于声纳探测的水下目标跟踪模型，并分析了洋流干扰对跟踪过程的影响。其次，将AUV集群视为水下自组织网络，基于软件定义网络（SDN）提出了一种面向AUV集群的新型多智能体强化学习（MARL）架构，通过集中管理与分布式操作提升了AUV集群的灵活性与可扩展性。基于所提MARL架构，我们进一步提出了“动态注意力切换”与“动态重采样切换”机制，以增强AUV集群在任务执行过程中的协同效率与准确性。最后，基于所提出的AUV分类方法，我们设计了一种名为ASMA的高效协同跟踪算法。评估结果表明，与近期多项研究成果相比，本文提出的跟踪算法在收敛速度与跟踪精度方面均能实现精确的水下多目标跟踪。