Deep Reinforcement Learning Based Accurate UUV Localization

Unmanned Underwater Vehicles (UUVs) are widely used in scenarios such as underwater resource exploration and underwater tactical surveillance. In order to improve mission efficiency and completion rate, it is necessary to enhance the positioning accuracy of UUVs. However, due to the influence of long transmission delay of underwater acoustic signals, bending of sound lines, change of sound velocity and ocean current movement, there are still many challenges to accurately locate UUV in motion. In this paper, we propose a deep reinforcement learning based accurate UUV localization (DRLAUL) algorithm. DR-LAUL algorithm is capable of recognizing received signals even in the presence of non-line-of-sight paths. It prioritizes line-of-sight signals for localization and compensates for the propagation delay of localization signals, enhancing distance estimation accuracy, improving localization precision, and reducing the overall system energy consumption. The simulation results show that compared to two benchmark algorithms, the DRLAUL algorithm reduces the RMSE by 66.1% and 38.1% respectively, and reduces energy consumption by 50.6% and 23.5% respectively. Furthermore, the DRLAUL algorithm achieves the best performance in different underwater environments.