Computer Science ›› 2019, Vol. 46 ›› Issue (12): 120-125.doi: 10.11896/jsjkx.181202381

• Network & Communication • Previous Articles     Next Articles

RFID Indoor Positioning Algorithm Combining Grasshopper Optimization Algorithm and Extreme Learning Machine

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

  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:2018-12-13 Online:2019-12-15 Published:2019-12-17

Abstract: With the rapid development of indoor positioning technology,radio frequency identification (RFID) technology has become the preferred solution due to its advantages of non-contact and rapid identification.However,the accuracy of existing RFID indoor positioning algorithms is easily affected by the tag density and algorithm efficiency,and environmental adaptation of existing algorithms is not strong enough.Therefore,this paper introduced an RFID indoor positioning algorithm based on the grasshopper optimization algorithm (GOA) fused with extreme learning machine (ELM).The algorithm is proposed to tune the input layer weight and hidden layer threshold biases randomly generated by the extreme learning machine,so that it can reduce learning time in the offline phase.At the same time,the algorithm can effectively resist the environmental interference and overcome the change of signal strength value on the positioning accuracy.Experiments are carried out to study the influence factors and validate the performance.Both the simulation and test experiment results show that compared with NN-based algorithm and NMDS-RFID algorithm,the average positioning error of the proposed algorithm is reduced by 22.32% and 20.06% respectively,and the average execution time is reduced by 58.7% and 7.55% respectively.GOA-ELM indoor positioning algorithm can achieve more accurate positioning results and has certain adaptability to the changes of the environment.

Key words: Extreme learning machine, Grasshopper optimization algorithm, Indoor positioning, Radio frequency identification, Received signal strength indicator

CLC Number: 

  • TP301.6
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