Exploiting high-precision AoA estimation method using CSI from a single WiFi station

Abstract

The accuracy of estimating the angle of arrival (AoA) using wireless fidelity (WiFi) channel state information (CSI) has been a topic of intense interest in the fields of the Internet of Things, location-based services, etc. We propose a high-precision method of AoA estimation of the direct path (DP) using WiFi CSI from a single station. It contains three stages: data preprocessing, AoA-time of flight (ToF) joint estimation for all paths, and the DP's AoA estimation. Firstly, phase calibration, linear transform, and multiple-layer filtering are accordingly conducted after CSI collection in the preprocessing stage to output the denoised CSI. Then, the AoA and ToF values for all paths are simultaneously obtained utilizing a spatial smoothing multiple signal classification (MUSIC) algorithm. Finally, the density-based spatial clustering for noise applications (DBSCAN) algorithm divides all the AoA and ToF values into several clusters. The target cluster that meets the requirements of maximum counts and minimum mean ToF is subsequently selected. The weighted centroid AoA value of the target cluster is regarded as the AoA of the DP. AoA estimation experiments using different sampling packets are conducted in a small conference room with an Intel 5300 network interface card along a straight line. The proposed method could recognize the DP with a rate of 100 percent and estimate the AoA of the DP with a mean absolute error of 2° and root mean square error of 2.82° Compared with SpotFi and hierarchical clustering–logistic regression systems, the proposed method improves AoA estimation accuracy by at least 75 %. Therefore, the proposed method could achieve a high-precision estimation of the AoA of the DP in the case 26 of different short distances.