Underwater Acoustic Aided Grid Localization of Multi-UUVs with Reinforcement Learning

Underwater acoustic (UWA) communication and localization systems have been used to improve the self-localization accuracy of mobile unmanned underwater vehicles (UUVs), However, poor UWA channels, sound velocity estimation errors and sound ray bending, and moving UUVs cause great difficulties in the timeliness and accuracy of multi-UUVs localization. In this paper, we apply reinforcement learning (RL) to the localization process of multi-UUVs and propose a high-precision localization scheme, named RL-GLMU. In this scheme, the localization process is achieved by modulated UWA communication in the absence of precise ocean information, and the signal weights and locations of the localization process are optimized by RL to improve the localization accuracy. Then, the learning space is discretized by topological gridding, which improves the convergence speed of the algorithm. Simulation results compared with the benchmark algorithms verify the effectiveness and robustness of the scheme, which effectively improves the localization accuracy and reduces the time delay error and localization time.