Computer Science ›› 2021, Vol. 48 ›› Issue (3): 188-195.doi: 10.11896/jsjkx.200600134

• Artificial Intelligence • Previous Articles     Next Articles

Prediction of RFID Mobile Object Location Based on LSTM-Attention

LIU Jia-chen, QIN Xiao-lin, ZHU Run-ze   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Received:2020-06-22 Revised:2020-09-17 Online:2021-03-15 Published:2021-03-05
  • About author:LIU Jia-chen,born in 1996,postgra-duate.His main research interests include location prediction of moving object and data mining,etc.
    QIN Xiao-lin,born in 1953,professor,Ph.D,is a senior member of China Computer Federation.His main research interests include spatial and spatio-temporal databases,data management and security in distributed environment,etc.
  • Supported by:
    National Natural Science Foundation of China(61728204).

Abstract: With the continuous development of radio frequency identification (RFID) technology,due to its advantages of high accuracy and large amount of data information compared to global positioning system (GPS),the application of RFID to intelligent transportation to predict the location of moving objects attracts widespread attention.However,due to the discrete distribution of its positioning base stations,the different influences weight of different base stations on position prediction,and the long-term historical information will bring dimensional disasters and other issues,and the position prediction of mobile objects is facing severe challenges.In response to these challenges,based on the analysis of the shortcomings of existing prediction algorithms,a machine learning model combining long short-term memory (LSTM) and attention mechanism is proposed.This algorithm reduces the dimension of the input vector encoded by one-hot through the neural network,and uses the attention mechanism to explore the weighting effect of different positioning base stations on position prediction,and finally performs position prediction.Compa-rative experiment on the RFID data set provided by Nanjing Traffic Management Bureau shows that compared with the existing algorithms,the LSTM-Attention algorithm has a significant improvement in prediction accuracy.

Key words: Attention mechanism, Dimension reduction, Location prediction, Long short-term memory, Radio frequency identification

CLC Number: 

  • TP311
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