Robust Sub-Meter Level Indoor Localization with a Single WiFi Access Point-Regression Versus Classification

Chenlu Xiang, Shunqing Zhang*, Shugong Xu, Xiaojing Chen, Shan Cao, George C. Alexandropoulos, Vincent K.N. Lau

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

38 Citations (Scopus)

Abstract

Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cramér-Rao lower bound (CRLB) assisted robust training, which achieves more robust sub-meter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.

Original languageEnglish
Article number8862951
Pages (from-to)146309-146321
Number of pages13
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019
Externally publishedYes

Keywords

  • channel state information
  • deep learning
  • Indoor localization
  • logistic regression
  • WiFi

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