Computer Science ›› 2020, Vol. 47 ›› Issue (2): 233-238.doi: 10.11896/jsjkx.190100070

• Computer Network • Previous Articles     Next Articles

RFID Indoor Positioning Algorithm Based on Asynchronous Advantage Actor-Critic

LI Li,ZHENG Jia-li,WANG Zhe,YUAN Yuan,SHI Jing   

  1. (School of Computer,Electronics and Information,Guangxi University,Nanning 530004,China)1;
    (Guangxi Key Laboratory of Multimedia Communications and Network Technology,Nanning 530004,China)2
  • Received:2019-01-10 Online:2020-02-15 Published:2020-03-18
  • About author:LI Li,born in 1994,postgraduate.Her main research interests include information processing and communication networks,reinforcement learning and internet of things;ZHENG Jia-li,born in 1979,professor.His main research interests include internet of things,RFID and artificial intelligence.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61761004).

Abstract: In view of the fact that the accuracy of existing RFID indoor positioning algorithm is easily affected by environment factors and the robustness is not strong,this paper proposed an RFID indoor positioning algorithm based on asynchronous advantage actor-critic (A3C).The main steps of the algorithm are as follows.Firstly,the RSSI value of RFID signal strength is used as the input value.The multi-thread sub-action network parallel interactive sampling learning,and the sub-evaluation network evaluates the advantage and disadvantage of the action value,so that the model is continuously optimized to find the best signal strength RSSI and trains the positioning model.The sub-thread network updates the network parameters to the global network on a regular basis,and the global network finally outputs the specific location of the reference tag,at the same time the asynchronous advantage actor-critic positioning model is trained.Secondly,in the online positioning stage,when the target to be tested enters the area to be tested,the signal strength RSSI value of the object to be tested is recorded and input into the asynchronous advantage actor-critic positioning model.The sub-thread network obtains the latest positioning information from the global network,locates the side target,and finally outputs the specific position of the target.RFID indoor positioning algorithm based on asynchronous advantage actor-critic was compared with the traditional RFID indoor positioning algorithm based on Support Vector Machines (SVM) positioning,Extreme Learning Machine (ELM) positioning,and Multi-Layer Perceptron positioning (MLP).Experiment results show that the mean positioning error of the proposed algorithm is respectively decreased by 66.114%,50.316% and 44.494%; the average positioning stability is respectively increased by 59.733%,53.083% and 43.748%.The experiment results show that the proposed RFID indoor positioning algorithm based on asynchronous advantage actor-critic has better positioning performance when dealing with a large number of indoor positioning targets.

Key words: RFID, RSSI, Reinforcement learning, Asynchronous advantage actor-critic, Indoor positioning

CLC Number: 

  • TP301.6
[1]SHI J Y,QIN X L,WANG L.Gradient and Constant-game Based RFID Indoor Localization Algorithm[J].ComputerScience,2015,42(11):138-143.
[2]ZHENG J,YANG Y,HE X,et al.Multiple-port reader antenna with three modes for UHF RFID applications[J].Electronics Letters,2018,54(5):264-266.
[3]LIU K,ZHANG W,ZHANG W D,et al.A Wireless Positioning Method Based on Deep Neural Network[J].Computer Engineering,2016,42(7):82-85.
[4]YANG Y N,XIA B,YUAN W,et al.Research on Ranging Algorithm Based on Convolution Neural Network[J].Journal of Chongqing University of Technology(Natural Science),2018(3):172-177.
[5]WANG C,WU F,SHI Z,et al.Indoor positioning technique by combining RFID and particle swarm optimization-based back propagation neural network[J].Optik - International Journal for Light and Electron Optics,2016,127(17):6839-6849.
[6]WANG C,SHI Z,WU F,et al.An RFID indoor positioning system by using Particle Swarm Optimization-based Artificial Neural Network[C]∥2016 International Conference on Audio.Language and Image Processing(ICALIP).IEEE Computer Society,2017:738-742.
[7]KUNG H Y,CHAISIT S,PHUONG N T M.Optimization of an RFID location identification scheme based on the neural network[J].International Journal of Communication Systems,2015,28(4):625-644.
[8]JIANG X,LIU J,CHEN Y,et al.Feature Adaptive Online Sequential Extreme Learning Machine for lifelong indoor localization[J].Neural Computing & Applications,2016,27(1):215-225.
[9]LIU F,ZHONG D.GSOS-ELM:An RFID-Based Indoor Localization System Using GSO Method and Semi-Supervised Online Sequential ELM[J].Sensors,2018,18(7):1995.
[10]GAO Z,MA Y,LIU K,et al.An Indoor Multi-tag Cooperative Localization Algorithm Based on NMDS for RFID[J].IEEE Sensors Journal,2017,17(7):2120-2128.
[11] ZHAO Y,LIU K,MA Y,et al.Similarity Analysis-Based Indoor Localization Algorithm With Backscatter Information of Passive UHF RFID Tags[J].IEEE Sensors Journal,2016,17(99):1-1.
[12]SUTTON R,BARTO A.Reinforcement Learning:An Introduction(second edition)[M].The MIT Press,2018.
[13]MURRAY D G,MURRAY D G.A computational model for TensorFlow:an introduction[C]∥Proceesings of the 1st ACM SIGPLAN International Workshop on Machine Learning and Programming Language.New York:ACM,2017:1-7.
[14]ABADI M.TensorFlow:learning functions at scale[J].Acm Sigplan Notices,2016,51(9):1-1.
[15]SCHMIDHUBER J.Deep learning in neural networks:An overview[J].Neural Network,2015,61(5):85-117.
[16]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[J].arXiv:1409.1556,2014.
[17]SONG R,LEWIS F,WEI Q,et al.Multiple actor-critic struc-tures for continuous-time optimal control using input-output data[J].IEEE Transactions on Neural Networks and Learning Systems,2015,26(4):851-865.
[18]MNIH V,BADIA A P,MIRZA M,et al.Asynchronous Methods for Deep Reinforcement Learning[J].arXiv:1602.01783v2,2016.
[19]BURTON A,PARIKH T,MASCARENHAS S,et al.Driver identification and authentication with active behavior modeling[C]∥12th International Conference on Network and Service Management(CNSM).IEEE Computer Society,2017:388-393.
[20]ALARIFI A,ALSALMAN A M,ALSALEH M,et al.Ultra Wideband Indoor Positioning Technologies:Analysis and Recent Advances[J].IEEE Sensors,2016,16(5):1-36.
[21]ZHAI X,ALI A A S,AMIRA A,et al.MLP Neural Network Based Gas Classification System on Zynq SoC[J].IEEE Access,2017,4(99):8138-8146.
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