Improving indoor positioning precision by using received signal strength fingerprint and footprint based on weighted ambient Wi-Fi signals

Abstract

Positioning is the foremost process in the location based service (LBS). With the GPS signal strength obstructed by the wall, indoor users cannot obtain their positions with the assistance from the global positioning satellites. Most of the indoor positioning systems have relied on received signal strengths (RSSs) from indoor wireless emitting devices, such as Wi-Fi access points (APs). Integrating indoor position information into the application on the modern handheld devices can increase the application diversity and quality in an indoor environment. In this paper, we propose a novel indoor positioning scheme assisted by the RSS fingerprint and footprint. Smartphone users can get their indoor position based on RSSs from the surrounding Wi-Fi APs. With the assistance of collecting ambient Wi-Fi RSSs from not only the intrinsic APs but also the extrinsic APs, filtering RSSs by directions/orientations, and mitigating signal fluctuation, our proposed scheme can overcome the severe signal instability problem in the indoor environment and raise the positioning accuracy. In order to reduce the time complexity of the indoor positioning procedure, we design a close designated location set (CDLS) algorithm that only uses the designated locations with the similar footprints of current user's position to determine the user's location. The proposed RSS fingerprint and footprint matching mechanism can speed up the positioning process. Meanwhile, to lessen the possible negative effect of extrinsic APs, the weighted voting positioning (WVP) algorithm would assign higher reference weights to the signals from the intrinsic APs, and adjust the weights to the signals from the extrinsic APs by their failure probability. The evaluation results show that our proposed scheme can achieve a certain level of accuracy in the indoor environments and outperform other solutions.