Indoor localization is becoming an increasingly relevant topic as the world rapidly becomes more connected through existing and emerging wireless technologies. The use cases of location based services (LBS) are endless; including locating people and objects inside of hospitals, corporate headquarters, subway stations, manufacturing facilities, and more. Due to the wide range of applications for indoor localization, many solutions have been explored with important trade-offs in energy efficiency, accuracy, range, cost, and availability. Technologies including Wi-Fi, Radio Frequency Identification (RFID), and Ultra-Wideband (UWB) perform well in some metrics but poorly in others. Bluetooth low energy (BLE) technology performs well in most metrics except accuracy. Range-based solutions often use received signal strength (RSS) as it a free metric with compatible technologies (Bluetooth and Wi-Fi). However, RSS is prone to large error when converting to distance estimates due to multipath propagation and interference from obstructions in the environment. Still, the low cost of BLE technology allows for many nodes to be deployed and filtering and other machine learning techniques can be applied to achieve improved localization. Several techniques for performing localization based on RSS of BLE nodes are explored. We explore algorithms based on nearest neighbors, extended Kalman filtering, and several supervised learning methods. A simulated environment is developed to quickly test the robustness of algorithms under varying conditions and assumptions. A discussion of trade-offs associated with using each technology and algorithm is presented.




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