@article{LIU2026, 
author = {Ting LIU and Hui WANG and Kai ZHENG and Qiqiang GAO},
title = {Probabilistic perception-based TS-DQN decision-making for autonomous USV antisubmarine search},
year = {2026},
journal = {Chinese Journal of Ship Research},
volume = {21},
number = {5},
pages = {353-366},
keywords = {unmanned vehicles, reinforcement learning, sonar detection probability model, dueling network, prioritized experience replay},
url = {https://www.sciopen.com/article/10.19693/j.issn.1673-3185.04440},
doi = {10.19693/j.issn.1673-3185.04440},
abstract = {ObjectiveThis study aims to develop a deep reinforcement learning-based search algorithm for unmanned surface vehicles (USVs) in submarine detection tasks.MethodFirst, a search environment and a kinematic model are constructed, and a sonar detection probability model incorporating the effects of both distance and angle is developed, with well-defined criteria for determining detection success. Based on this framework, the problem is formulated as a Markov decision process (MDP), and a deep Q-network (DQN) is employed to solve it. The state space explicitly includes detection probability, while a multi-objective reward function is designed to integrate detection probability, distance, and angle. To enhance learning efficiency, a temporal difference DQN with probabilistic sensing (TS-DQN) algorithm is proposed, combining a double-dueling network architecture with prioritized experience replay. Additionally, a probabilistic perception-based ε-greedy exploration strategy is implemented, enabling dynamic adjustment of exploration behavior based on real-time detection states, thereby significantly improving policy learning efficiency. ResultsExtensive simulation experiments demonstrate that the proposed method achieves a detection success rate of 38.85%, approximately 18 times that of the second-best method, Dueling DQN. The approach also reduces the average path length to 334.36 steps, shortening the search trajectory by more than 9.5% compared to other algorithms.ConclusionThe proposed algorithm exhibits significant advantages in detection efficiency and effectiveness, offering an effective approach for autonomous USV-based submarine search.}
}