Journal on Communications ›› 2021, Vol. 42 ›› Issue (11): 159-171.doi: 10.11959/j.issn.1000-436x.2021218

• Papers • Previous Articles     Next Articles

Indoor Wi-Fi fingerprint localization method based on CSI tensor decomposition

Mu ZHOU1,2, Yuexin LONG1,2, Qiaolin PU1,2, Yong WANG1,2, Wei HE1,2   

  1. 1 School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    2 Chongqing Key Laboratory of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • Revised:2021-09-27 Online:2021-11-25 Published:2021-11-01
  • Supported by:
    The Science and Technology Research Program of Chongqing Municipal Education Commission(KJZD-K202000605);The Science and Technology Research Program of Chongqing Municipal Education Commission(KJQN201900603);Chongqing Natural Science Foundation Project(cstc2020jcyj-msxmX0842);Chongqing Natural Science Foundation Project(cstc2020jcyj-msxmX0865);The National Natural Science Foundation of China(61901076);The National Natural Science Foundation of China(61704015);Postgraduate Scientific Research and Innovation Project of Chongqing(CYS21293)

Abstract:

Aiming at the problem that as the scale of the fingerprint database increases, the training cost and processing complexity of CSI fingerprints will also greatly increase, an indoor Wi-Fi fingerprint localization method based on CSI tensor decomposition was proposed.Firstly, the tensor decomposition algorithm based on the PARAFAC (parallel factor) analysis model and the ALS (alternate least squares) iterative algorithm were combined to reduce the interference of the environment.Then, the tensor wavelet decomposition algorithm was used to extract the feature and obtain the CSI fingerprint.Finally, a localization model was established based on the PLSR (partial least squares regression) algorithm to realize the location estimation.Experimental results show that the confidence probability of the proposed method is 94.88% within 4 m localization error, which verifies that the proposed method has good localization performance while fitting the relationship between CSI location fingerprints and location coordinates.

Key words: indoor localization, location fingerprint, channel state information, tensor decomposition, regression analysis

CLC Number: 

No Suggested Reading articles found!