A Multi-Scale Feature Selection Framework for WiFi Access Points Line-of-sight Identification

Research output: Contribution to conferencePaperpeer-review


Despite its high accuracy in the ideal condition where there is a direct line-of-sight between the Access Points and the user, most WiFi indoor positioning systems struggle under the non-line-of-sight scenario. Thus, we propose a novel feature selection algorithm leveraging Machine Learning based weighting methods and multi-scale selection, with WiFi RTT and RSS as the input signals. We evaluate the algorithm performance on a campus building floor. The results indicated an accuracy of 93% line-of-sight detection success with 13 Access Points, using only 3 seconds of test samples at any moment; and an accuracy of 98% for individual AP line-of-sight detection.
Original languageEnglish
Publication statusPublished - 12 May 2023


  • WiFi Round-Trip Time
  • feature selection
  • indoor positioning

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