Abstract
WiFi-based indoor positioning methods have attracted extensive attention due to the wide installation of WiFi access points (APs). Recently, the WiFi standard was modified and introduced into a new two-way approach based on round trip time (RTT) measurement, which brings some changes for indoor positioning based on WiFi. In this work, we propose a WiFi RTT positioning method based on line of sight (LOS) identification and range calibration. Given the complexity of the indoor environment, we design a non-line of sight (NLOS) and LOS identification algorithm based on scenario recognition. The positioning scenario is recognized to assist NLOS and LOS distances identification, and gaussian process regression (GPR) is utilized to construct the scenario recognition model. Meanwhile, the calibration model for LOS distance is presented to correct the measuring distance and the scenario information is utilized to constrain the estimated position. When there is a positioning request, the positioning scenario is identified with the scenario recognition model, and LOS measuring distance is obtained based on the recognized scenario. The LOS range measurements are first calibrated and then utilized to estimate the position of the smartphone. Finally, the positioning scenario is used to constrain the estimation location to avoid it beyond the scenario. The experimental results show that the positioning effect of the proposed method is far better than that of the Least Squares (LS) algorithm, achieving a mean error (ME) of 0.862 m and root-mean-square error (RMSE) of 0.989 m.
Highlights
Indoor positioning technology has attracted wide attention because of its massive market demand and huge economic prospects
non-line of sight (NLOS) and line of sight (LOS) identification can assist in the improvement of the accuracy of indoor positioning based on Wireless Fidelity (WiFi) round trip time (RTT), and the generation of NLOS may be caused by the cross-scene transmission of the signal
This paper proposes an indoor positioning method based on LOS identification and range calibration, which can recognize the positioning scene and identify the LOS distances based on the identified scenario
Summary
Indoor positioning technology has attracted wide attention because of its massive market demand and huge economic prospects. NLOS and LOS identification can assist in the improvement of the accuracy of indoor positioning based on WiFi RTT, and the generation of NLOS may be caused by the cross-scene transmission of the signal. This paper proposes a LOS identification approach based on scenario recognition to assist the WiFi RTT positioning in order to grow its accuracy. (2) A scenario recognition method based on GPR is proposed to recognize the positioning scene, which has no need to collect training data and uses the real distances between smartphones and access points (APs) as the training data.
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