This paper proposes a new hybrid positioning algorithm combining radio signal and IMU sensors. We describe the relationship between multiple measurements in mathematical form by a geometrical approach. To estimate the position, Wi-Fi round-trip time (RTT) and the Pedestrian Dead Reckoning (PDR) technique are used. Various field experiments verify the proposed algorithm in line-of-sight (LOS) guaranteed and LOS / non-LOS mixed environments.
Abstract: Recently, round-trip time (RTT) measured by a finetiming measurement protocol has received great attention in the area of WiFi positioning. It provides an acceptable ranging accuracy in favorable environments when a line-of-sight (LOS) path exists. Otherwise, a signal is detoured along with non-LOS paths, making the resultant ranging results different from the ground-truth, called an RTT bias, which is the main reason for poor positioning performance. To address it, we aim at leveraging the user mobility trajectory detected by a smartphone’s inertial measurement units, called pedestrian dead reckoning (PDR). Specifically, PDR provides the geographic relation among adjacent locations, guiding the resultant positioning estimates’ sequence not to deviate from the user trajectory. To this end, we describe their relations as multiple geometric equations, enabling us to render a novel positioning algorithm with acceptable accuracy. Depending on the mobility pattern being linear or arbitrary, we develop different algorithms divided into two phases. First, we can jointly estimate an RTT bias of each AP and the user’s step length by leveraging the geometric relation mentioned above. It enables us to construct a user’s relative trajectory defined on the concerned AP’s local coordinate system. Second, we align every AP’s relative trajectory into a single one, called trajectory alignment, equivalent to transformation to the global coordinate system. As a result, we can estimate the sequence of the user’s absolute locations from the aligned trajectory. Various field experiments extensively verify the proposed algorithm’s effectiveness that the average positioning error is approximately 0.369 (m) and 1.705 (m) in LOS and NLOS environments, respectively.
Authors: K. Han, S. M Yu, S.-L. Kim, and S.-W Ko, "Exploiting User Mobility for WiFi RTT Positioning: A Geometric Approach," in IEEE Internet of Things Journal (Early Access), Apirl 2021. [Online].
preprint available: https://arxiv.org/abs/2011.03698