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

被引：4

作者：

Feng, Xu ^{[1
]}

Khuong An Nguyen ^{[1
]}

Luo, Zhiyuan ^{[2
]}

机构：

[1] Univ Brighton, Comp & Maths Div, Brighton BN2 4GJ, E Sussex, England

[2] Royal Holloway Univ London, Dept Comp Sci, Surrey TW20 0EX, England

来源：

2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC | 2023年

关键词：

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

D O I：

10.1109/WCNC55385.2023.10118876

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：6

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