OpenPose-Inspired Reduced-Complexity CSI-Based Wi-Fi Indoor Localization

被引:0
作者
Mahmoud, Mohamed Hany [1 ]
Shoudha, Shamman Noor [1 ]
Abdallah, Mohamed [2 ]
Al-Dhahir, Naofal [1 ]
机构
[1] Univ Texas Dallas, Elect & Comp Engn Dept, Richardson, TX 75080 USA
[2] Hamad Bin Khalifa Univ, Coll Sci & Engn, Div Informat & Comp Technol, Doha, Qatar
关键词
Location awareness; Wireless fidelity; Accuracy; Mathematical models; Antenna arrays; Fingerprint recognition; Feature extraction; Indoor localization; WiFi; CSI; deep learning;
D O I
10.1109/LCOMM.2024.3433510
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Inspired by the OpenPose model in computer vision, we propose a reduced-complexity deep learning (DL) model for indoor localization based on WiFi Channel State Information (CSI) preprocessed with a 2D IFFT and transformed into 2D heatmap images. We mitigate timing offset due to transmitter-receiver miss-synchronization for both one-way and two-way CSI scenarios. Compared to state-of-the-art, our method improves accuracy by 72% at $90<^>{th}$ percentile while reducing DL model size by more than 90%. To our best knowledge, this is the first DL model for WiFi indoor localization based on learning and generalizing from CSI features instead of fingerprinting.
引用
收藏
页码:2066 / 2070
页数:5
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