Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (5): 277-283.doi: 10.11896/jsjkx.230300137

• Computer Network • Previous Articles     Next Articles

Indoor Location Algorithm in Dynamic Environment Based on Transfer Learning

WANG Jiahao1, FU Yifu1, FENG Hainan1, REN Yuheng2   

  1. 1 School of Information and Software Engineering,University of Electronic Science and Technology,Chengdu 610051,China
    2 School of Business,Belarusian State University,Minsk 220071,Republic of Belarus
  • Received:2023-03-16 Revised:2023-06-30 Online:2024-05-15 Published:2024-05-08
  • About author:WANG Jiahao,born in 1978,Ph.D,associate professor,is a member of CCF(No.27769M).His main research interests include IoT,information security and data mining.
    FU Yifu,born in 1998,master,is a member of CCF(No.67870G).His mainresearch interests include indoor location,transfer learning and data mining.
  • Supported by:
    UESTC-ZHIXIAOJING Joint Research Center of Smart Home(H04W210180),Neijiang Technology Incubation and Transformation Funds(2021KJFH004), Key R & D Program Key R & D Projects in High-tech Fields(2022YFG0212) and Science and Technology Support Plan of Sichuan Province of China(2021YFG0024).

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Abstract: With the development of smart home,the Wi-Fi signal-based localization technology has also been widely studied.In actual application,the training data and test data collected by indoor positioning algorithm usually do not come from the same ideal conditions.Changes in various environmental conditions and signal drift can cause different probability distributions between the training data and test data.The existing positioning algorithm cannot guarantee stable accuracy when facing these different probability distributions,resulting in dramatic reduction and infeasibility of the positioning accuracy of indoor location algorithms.Considering these difficulties,the domain adaptation technology in transfer learning is proven to be a promising solution in past researches to solve the inconsistent probability distributions problem.In this paper,a feature transferbased indoor localization algorithm TL-GLMA is proposed by combining domain adaptation learning and machine learning algorithms.TL-GLMA maps the original data of two domains to the high-dimension space through feature transfer,so as to minimize the distribution difference between the two domains in retaining the local geometric properties.In addition,because the mapped data is independent and identically distributed,TL-GLMA can use it for training the classifier to achieve better location result.Experiment results show that TL-GLMA can effectively reduce the interference caused by environmental changes and improve the location accuracy.

Key words: Indoor location, Wi-Fi signal, Environmental adaptation, Transfer learning, Domain adaptation

CLC Number: 

  • TP393
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