Wi-Fi RTT based indoor positioning with dynamic weighted multidimensional scaling

Indoor positioning methods have appeared to fulfill indoor location-based systems requirements, it is still a great challenge to obtain high precision results of indoor positioning. For example, fingerprint-based methods reach high performances but have a high cost for to survey the environment in order to collect sample and to maintain location fingerprints. Systems based on log-distance path loss model suffer from the multi-path problem and the adjustment of Wi-Fi station powers, and achieve low accuracy in complex environments. The appearance of fine time measurement protocol supported Wi-Fi access points provide a novel way to develop accurate indoor positioning algorithms. Considering the influence of the indoor multi-path effect to the fine time measurement ranging accuracy, we propose a multi-dimensional scaling based positioning algorithm to reduce the impact of ranging errors. We leverage the multidimensional scaling algorithm to estimate the rough position of positioning clients. Successively, adjusting the weight of fine time measurement ranging, we optimize the positioning results with the application of a SMACOF strategy. Through experiments conducted in a complex real-world scenario, we demonstrate that the system proposed reach an accuracy below the 2.5 meters at 80% of the cases.