.png){kind=icon}
返回
Efficient Beacon-Aided AUV Localization: A Reinforcement Learning Based Approach
.png){kind=icon}
PRE
AI
DOI：10.1109/TVT.2024.3360262.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Beacon-aided autonomous underwater vehicle (AUV) localization supporting maritime surveillance applications in underwater acoustic sensor networks selects a fixed number of beacons with constant transmit power, and thus has degradation of localization accuracy with severe channel fading and position fluctuation of beacons. In this paper, we propose a reinforcement learning based AUV localization scheme to choose the beacons and their transmit power to improve the localization accuracy and energy efficiency based on the AUV depth, the received signal strength, the number of selected beacons and the beacon energy consumption. According to the least squares method, the AUV position is calculated based on the isogradient sound speed model and the round-trip time of the localization signals. The localization error averaged over different beacon sets is evaluated to formulate the localization policy distribution. Deep neural network is designed to estimate the expected long-term discounted utility with higher feature extraction efficiency for the underwater networks with a large number of beacons. The Cramer-Rao lower bounds of the proposed localization schemes are derived to analyze the effect of the position fluctuation of beacons on the localization accuracy. Simulation results verify the performance gain in terms of the localization accuracy and the beacon energy consumption over the benchmark.
Keyword:
Location awareness
Estimation
Energy consumption
Simulation
Signal processing algorithms
Position measurement
Underwater acoustics
Underwater acoustic sensor networks
localization
autonomous underwater vehicles
Cramer-Rao lower bound
reinforcement learning
期刊
IF:
7.1
论文数:
1.8W
被引数:
6.6W
机构
引用论文
A Survey of Architectures and Localization Techniques for Underwater Acoustic Sensor Networks水声传感器网络的体系结构和定位技术综述
.png){kind=icon}
PREAI
分享收藏Study of bi-directional buck-boost converter topologies for application in electrical vehicle motor drives应用于电动汽车电机驱动的双向buck-boost变换器拓扑研究
PREAI
分享收藏
分享收藏AUV-Aided Joint Localization and Time Synchronization for Underwater Acoustic Sensor NetworksAUV辅助的水声传感器网络联合定位与时间同步
PREAI
分享收藏
查看更多内容