Localization of access points based on the Rayleigh lognormal model

Abstract

Acquiring the knowledge of WiFi access point (AP) locations not only plays a vital role in various WiFi related applications, such WiFi-based indoor localization, the deployment of new WiFi APs, and so on, but also contributes to the emergence of novel applications. Most existing studies assume the well-known lognormal shadowing model, which only reflects large-scale fading in WiFi signal propagations but ignores small-scale fading induced by pervasive multipath effects. In this paper, we tackle the problem of AP localization based on the Rayleigh lognormal model which characterizes the influence of both large-scale and small-scale fading. Provided that a participant holding a smartphone is walking along a path and the smartphone automatically and continuously collects received signal strength (RSS) measurements from a target AP at known positions, particle filtering is applied to sequentially narrow the scope of possible locations of as well as the propagation parameters of the wireless signals emitted by the target AP, and the weighted mean of all candidate locations is returned as its final location estimate. Extensive experiments are carried out in typical indoor and outdoor scenarios, and reveal that the proposed method outperforms the solutions based on the lognormal model by 14.13%–70.38%.