End-to-end multi-sensor fusion method based on deep reinforcement learning in UASNs

Abstract

Underwater acoustic sensor networks (UASNs) have attracted considerable attention and are extensively employed for tracking underwater targets. However, due to the complex dynamics of underwater tracking environments, establishing accurate correlations between sensors remains elusive and challenging to precisely model. Traditional multi-sensor fusion methods for handling unknown correlations often face issues related to dependence on prior knowledge and a lack of flexibility. In response, we propose a novel end-to-end multi-sensor fusion algorithm rooted in deep reinforcement learning (DRL). To address the challenge of unknown correlations among sensors in UASNs, we formulate the multi-sensor fusion strategy as a Markov Decision Process (MDP) within the framework of reinforcement learning. The Proximal Policy Optimization (PPO) method is implemented to tackle this MDP without scalability limitations, aiming to derive an optimal sensor scheduling policy for UASNs. To enhance practical applicability, we adopt a mock data approach for algorithm training, eliminating the necessity for ground truth information on non-cooperative targets. Finally, both simulation and real-world experimental results demonstrate the superior performance of the proposed algorithm. It attains an improvement of at least 15% in tracking accuracy and exhibits a notable enhancement in stability when contrasted with traditional algorithms.