Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (4): 274-281.doi: 10.11896/jsjkx.200300028

• Computer Network • Previous Articles     Next Articles

RFID Indoor Positioning Algorithm Based on Proximal Policy Optimization

LI Li, ZHENG Jia-li, LUO Wen-cong, QUAN Yi-xuan   

  1. School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China
    Guangxi Key Laboratory of Multimedia Communications and Network Technology,Nanning 530004,China
  • Received:2020-06-24 Revised:2020-06-29 Online:2021-04-15 Published:2021-04-09
  • About author:LI Li,born in 1994,postgraduate.Her main research interests include information processing,communication networks,reinforcement learning and Internet of things.(1114235262@qq.com)
    ZHENG Jia-li,born in 1979,professor.His main research interests include Internet of things,RFID and artificial intelligence.
  • Supported by:
    National Natural Science Foundation of China(61761004) and Natural Science Foundation of Guangxi Province,China(2019GXNSFAA245045).

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Abstract: In the Radio Frequency Identification(RFID) dynamic indoor positioning environment,the positioning error and the computing complexity of traditional indoor positioning model will increase with the increase of the number of positioning targets.This paper proposes an RFID positioning algorithm based on Proximal Policy Optimization(PPO),which regards the positioning as Markov decision-making process.Firstly,the action evalution is combined with random action and the return of the action is then maximized to select the best coordinate value.Meanwhile,under the premise of limiting the action to a certain range,the algorithm introduces clipped probability ratios,using post-sample and pre-sample action alternatesly,then,with stochastic gradient ascent updates multiple epochs policy of minibatch and with the critic network evaluate the action.Finally,the PPO positioning model is obtained by training.This method can effectively reduce the positioning error and improve the positioning efficiency.At the same time,it has a faster convergence speed,especially when dealing with a large number of positioning targets,it can greatly reduce the computational complexity.Experiment results show that,compared with other RFID indoor positioning algorithms,such as Twin Delayed Deep Deterministic policy gradient(TD3),Deep Deterministic Policy Gradient(DDPG) and actor-critic using Kronecker-Factored Trust Region(ACKTR),the average positioning error of the proposed method decreases respectively by 36.361%,30.696% and 28.167%,the positioning stability improves by 46.691%,34.926% and 16.911%,and the computing complexity decreases respectively by 84.782%,70.213% and 63.158%.

Key words: Clipped probability ratios, Deep reinforcement learning, Indoor positioning, RFID

CLC Number: 

  • TP301.6
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