Adaptive Integrated Indoor Pedestrian Tracking System Using MEMS sensors and Hybrid WiFi/Bluetooth-Beacons With Optimized Grid-based Bayesian Filtering Algorithm

With recent dramatic increase in sensors deployments and processing nodes, accurate indoor positioning, tracking, and navigation is becoming achievable. Among many platforms that need to be localized and tracked are pedestrians. A reliable indoor pedestrians tracking has a wide range of applications such as healthcare, retail, rescue missions and context-awareness applications. This paper introduces a calibration-free hybrid indoor positioning system that utilizes inertial sensors (INS), wireless local area networks (WLAN), and low-cost Bluetooth low-energy (BLE) wireless beacons. BLE beacons are becoming very popular in retails and they can be easily installed in any indoor environment. To deal with

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