Device-Free WLAN Based Indoor Localization Scheme With Spatially Concatenated CSI and Distributed Antennas

Various machine learning (ML) based localization schemes using channel state information (CSI) in wireless local area networks (WLANs) have been investigated recently. Adopting a proper feature selection technique is important to achieve further improvement in detection accuracy. As described herein, we propose a device-free indoor localization scheme using a lightweight ML model with compressed spatially concatenated CSI in WLAN systems with distributed antennas. In this scheme, feedback beam-forming weights (BFWs) are collected at a CSI capture terminal. Then, current and past BFWs are concatenated as accurate feature data to characterize the object behavior. Additionally, we propose the use of a frequency-domain sampling scheme for a low-complexity real-time target position detection with a small number of datasets. Using ray-trace based simulation analysis and experimentally obtained results from an indoor environment, we demonstrate that the proposed scheme using the concatenated CSI is effective not only for achieving more accurate real-time detection, but also for reducing the necessary complexity for both off-line training and on-line classification compared with other reference schemes.