  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.
