AutoCali: Enhancing AoA-based Indoor Localization through Automatic Phase Calibration
Pengfei Yin, Dongheng Zhang, Tianyu Zhang, Shuai Yang, Guanzhong Wang, Yang Hu, Yan Chen
Abstract
Recent advancements in WiFi indoor localization have demonstrated the potential for achieving decimeter-level accuracy based on Angle of Arrival (AoA). However, existing commercial WiFi Access Points (APs) suffer from phase offset across different antennas, which significantly degrade the performance of AoA-based methods in practical deployment. Previous work either relied on labor-intensive manual calibration or involved inaccurate and non-robust automatic calibration. In this paper, we propose AutoCali, an accurate and robust automatic phase offset calibration system. The key insight is to utilize the binary nature of phase offsets and the property that triangulation exhibits higher convergence when the correct combination of phase offsets is employed. Extensive experiments demonstrate that AutoCali outperforms state-of-the-art methods by 22.1% in median localization error for simple scenarios and by 37.1% for complex multipath scenarios.