Asynchronous Localization for Underwater Acoustic Sensor Networks: A Continuous Control Deep Reinforcement Learning Approach

The localization of underwater acoustic sensor networks (UASNs) has emerged as a critical research area in the marine information fusion field. Generally, the convex optimization method is adopted to solve the localization problem. However, this method has limitations in complex underwater environments, since it is difficult to transform the nonconvex optimization problem into a convex optimization problem under such conditions. Recently, deep reinforcement learning (DRL) has shown great potential and promise in solving intricate optimization tasks. Motivated by this, we propose to adopt DRL for UASNs localization to improve accuracy and robustness. The key challenge is that existing DRL-based methods require discretization of the environment, which leads to a compromise between search time and localization precision. To address this challenge, we first model the localization problem as a Markov decision process (MDP) with continuous state and action spaces and subsequently introduce a continuous control DRL framework to solve the localization problem. Within this framework, we develop three continuous control DRL-based localization estimators to address the localization problem in unsupervised, supervised, and semisupervised scenarios. Comprehensive simulations demonstrate the effectiveness of our approach, as the proposed solutions exhibit several advantageous features compared to traditional methods, such as: 1) compared with the convex optimization-based method, the convex relaxation is not required; 2) compared with the least squares method, the proposed estimators are capable of converging to a global optimal state; and 3) compared with the discrete control DRL method, the proposed estimators reduce localization time and enhance localization accuracy significantly.