Multiple WiFi Access Points Co-Localization Through Joint AoA Estimation
Shuai Yang, Dongheng Zhang, Ruiyuan Song, Pengfei Yin, Yan Chen
Abstract
Indoor localization is a fundamental task to many real-world applications, which however remains unresolved, especially with commodity WiFi Access Points (APs). In this paper, we tackle this problem and propose an accurate, robust, and real-time indoor localization system that can be directly deployed on commodity WiFi infrastructure. Specifically, the proposed system makes three key contributions: 1) we introduce a non-parametric metric to measure the accuracy of Angle of Arrival (AoA) estimation; 2) we are the first to explicitly consider the relationship among the AoAs of different APs and propose a multiple APs co-localization algorithm to exploit such a relationship to improve the localization performance; 3) we propose several strategies to reduce the computational complexity of our system to achieve real-time localization. Extensive experiments are conducted to evaluate the performance of the proposed system under various situations, which demonstrate that the proposed system can achieve a 4 degrees median error of AoA estimation and 30 cm localization median error, outperforming the state-of-the-art systems.