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In recent years, indoor positioning systems (IPSs) have gained significance across various applications, including asset tracking, monitoring, interior navigation, and location-based services. The use of Wi-Fi-based technology is a popular choice for IPS due to its cost-effectiveness and widespread accessibility. The Wi-Fi signal’s round trip time (RTT) measurement, using the fine time measurement (FTM) protocol, offers fewer ranging errors in line-of-sight (LOS) conditions. However, Wi-Fi RTT ranging measurements encounter higher ranging errors in non-line of sight (NLOS), multipath, and interference scenarios. This study examines the error in ranging measurements for different scenarios such as LOS, glass, metal, and wall blocking scenarios. To address these challenges, we propose a method that combines calibrated RTT range and processed received signal strength (RSS) feature values in constructing a fingerprinting map to enhance positioning accuracy. The methodology includes the development of a range compensation model for RTT range calibration, utilizing a Gaussian filter for RSS measurement values processing, and creating a classifier model to distinguish between LOS and wall scenarios. This integrated approach reduces noise in measurement values, and the Gaussian process regression (GPR) algorithm is utilized to predict the final location of the user. Our proposed method achieved a positioning error of 0.79 m, surpassing the performance of RTT fingerprinting by 17.71%, RSS fingerprinting by 49.68%, and trilateration methods by 29.46%.
Rana et al. (Wed,) studied this question.
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