1. 3D localization of machines and tools based on deep reinforcement learning
- Author
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Ziqiang Lu, Aisheng Yang, Shen Zhou, Liangan Yao, Kai Sun, Ruikai Zhu, and Xianhong Jiao
- Subjects
History ,business.industry ,Computer science ,Reinforcement learning ,Artificial intelligence ,business ,Computer Science Applications ,Education ,3d localization - Abstract
In view of the importance of obtaining the position information of machines and tools in the collection and safety control of incoming and outgoing information of power equipment, a wireless three-dimensional positioning method based on deep reinforcement learning DRL is proposed. Modeling is based on the signal non-line-of-sight(NLOS) propagation model, which will be based on measurement. The process of target location features such as angle (AOD), angle of arrival (AOA) and time of arrival (TOA) is modeled as Markov decision process, and the target location features are taken as input and processed in DRL framework. Finally, the repeated training deep Q-network(DQN) algorithm is used for predictive positioning. The simulation results show that the advantages of using DRL in wireless positioning are verified.
- Published
- 2021
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