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• W3209307112 abstract "The underwater passive localization can be applied to underwater cooperative combat system, underwater environment detection and underwater resource exploration, which has important practical significance. Owing to sound velocity variation, sound line bending, non-line-of-sight transmission, clock drift, limited bandwidth, and long propagation time delay in the underwater environment, there are many errors in the estimation of the distance, which reduces acoustic localization accuracy. In this paper, a mobile hidden passive localization (RMHPL) algorithm based on reinforcement learning is proposed to reduce the energy consumption of the hidden mobile node and localization error. With regard to the varying acoustic propagation parameters and non-line-of-sight transmission, the proposed algorithm selects the time window based on reinforcement learning without knowing the underwater environment model. The Dyna-PS architecture is applied to reduce the convergence time and improve planning efficiency of the algorithm. Simulation results show that the proposed algorithm can improve the localization accuracy and reduce energy consumption of hidden mobile node." @default.
• W3209307112 created "2021-11-08" @default.
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• W3209307112 date "2021-08-17" @default.
• W3209307112 modified "2023-09-23" @default.
• W3209307112 title "Reinforcement Learning Based Energy Efficient Underwater Passive Localization of Hidden Mobile Node" @default.
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• W3209307112 doi "https://doi.org/10.1109/icspcc52875.2021.9564538" @default.
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