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.
|Publication status||Published - 26 Mar 2023|
|Event||IEEE Wireless Communications and Networking Conference (WCNC) 2023 - Glasgow, United Kingdom|
Duration: 26 Mar 2023 → 29 Mar 2023
|Conference||IEEE Wireless Communications and Networking Conference (WCNC) 2023|
|Abbreviated title||IEEE WCNC 2023|
|Period||26/03/23 → 29/03/23|