Exploiting User Mobility for WiFi RTT Positioning: A Geometric Approach

Abstract

Due to the massive deployment of WiFi APs and its accessibility to various positioning elements, WiFi positioning is a key enabler to provide seamless and ubiquitous location services to users. There are various kinds of WiFi positioning technologies, depending on the concerned positioning element. Among them, round-trip time (RTT) measured by a fine-timing measurement protocol has received great attention recently. It provides an acceptable ranging accuracy near the service requirements 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 groundtruth. The difference between the two is called an RTT bias, which is the main reason for poor positioning performance. To address it, we aim at leveraging the history of user mobility 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. The algorithm is designed into two phases. First, an RTT bias of each AP can be compensated by leveraging the geometric relation mentioned above. It provides a user's relative trajectory defined on the local coordinate system of the concerned AP. Second, the user's absolute trajectory can be found by rotating every relative trajectory to be aligned, called trajectory alignment. The proposed algorithm gives a unique position when the number of detected steps and APs is at least 4 and 3 for linear mobility and 5 and 2 for arbitrary mobility. 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.

Enhanced Wi-Fi RTT Ranging: A Sensor-Aided Learning Approach

“…Initially, Banin et al demonstrated the positioning accuracy [29] and investigated low-level fundamentals, such as the impact of clock drift on ranging performance [30]. Additionally, both ranging and positioning performances have been widely evaluated using the FTM protocol in various indoor and outdoor environments [31]- [48]. Certain studies investigated methods to improve the ranging accuracy using both RSS and RTT measurements [33], [37] or multiple RTT measurements obtained from different frequency bands [44].…”

“…In the positioning stage, Bayesian filtering techniques have been applied to track the location of mobile devices using a series of ranging results. In addition, inertial sensors incorporated in mobile devices can be used in this stage to improve the positioning accuracy further [32], [38], [40], [46], [48].…”