A novel underwater target tracking method in UASNs via collaborative deep reinforcement learning

Abstract

Modern underwater acoustic sensor networks (UASNs), as vital infrastructure for marine surveillance, face dual challenges in energy-efficient sensor scheduling and correlation-aware data fusion for underewater target tracking under resource-constrained conditions. Existing UASNs-based target tracking methods suffer from key limitations, including environment-dependent scheduling with poor adaptability, reliance on predefined correlation models for multi-sensor fusion, and the separate optimization of inherently coupled tasks. To address these issues, we develop a cooperative deep reinforcement learning (CDRL)-based framework for underwater target tracking that performs joint optimization through coordinated policy design. In this framework, a scheduling agent adaptively selects energy-efficient sensing platforms under dynamic conditions, while a fusion agent implements a model-free strategy to alleviate the need for precise correlation models. Both agents are trained using Proximal Policy Optimization (PPO) within a multi-agent coordinate architecture equipped with a global critic, enabling collaborative decision-making across tasks. In addition, a mock data method is introduced to reduce reliance on accurate ground truth, enhancing robustness against non-cooperative targets. Numerical simulation and real-world experiment confirm that the proposed framework consistently outperforms conventional approaches, achieving no less than a 15$\%$ improvement in energy efficiency.