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- W4386449971 abstract "Many crucial underwater tasks are performed based on the precise positions of target nodes in underwater acoustic sensor networks (UASNs). However, due to significant signal attenuation and long propagation latency in acoustic communication, it is challenging to achieve accurate and energy-efficient underwater localization. In this paper, we propose an autonomous underwater vehicles (AUVs) localization scheme to determine the selection and transmit power of beacons based on range measurements between AUVs and mobile beacons using the two-ray travel time of localization signals. Then, a safe reinforcement learning algorithm is proposed to select the AUV localization policy based on the AUV depth, the previous number of selected beacons and received signal strengths. Performance analysis in terms of computational complexity and Cramer-Rao lower bound is provided. Simulation results based on the isogradient sound speed model show that the proposed scheme achieves higher localization accuracy, lower energy consumption and higher utility compared with the benchmark." @default.
- W4386449971 created "2023-09-06" @default.
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- W4386449971 date "2023-08-10" @default.
- W4386449971 modified "2023-09-27" @default.
- W4386449971 title "Reinforcement Learning Based AUV Localization in Underwater Acoustic Sensor Networks" @default.
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- W4386449971 doi "https://doi.org/10.1109/iccc57788.2023.10233323" @default.
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