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Indoor wireless localization toward the next generation Wi-Fi access point has attracted considerable attention due to the presentation of the state-of-art Wi-Fi fine time measurement (FTM) protocol. In order to improve the autonomy, accuracy, and universality of wireless positioning based on the Internet of Things (IoT) terminals, this article proposes a hybrid wireless positioning system which contains the integration of Wi-Fi FTM, crowdsourced received signal strength indicator (RSSI) fingerprinting and micro-electro-mechanical-system (MEMS) sensors (H-WPS). A light-weight pedestrian aimed inertial navigation system (PINS) is proposed, which contains multilevel constraints and a global optimization model in order to eliminate the cumulative error caused by INS update. A deep-learning-based Wi-Fi fingerprinting database generation framework is developed for crowdsourced trajectories evaluation and selection. In addition, three different multisource integration models are applied to fuse the information of PINS, Wi-Fi FTM and RSSI fingerprinting, and calibrate the Wi-Fi ranging bias in real time, which is further enhanced by a novel misclosure check and the multilayer perceptron contained signal quality evaluation strategy. The comprehensive experiments demonstrate that the proposed H-WPS achieves much more precise and universal indoor positioning performance compared with the single location source, and meter-level localization precision can be realized in the Wi-Fi FTM-covered indoor scenes.
Yu et al. (Thu,) studied this question.