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A BLE RSSI ranking based indoor positioning system for generic smartphones

Indoor navigation in physical retail type spaces aids the navigation of users to find physical items at known destinations. WiFi Fingerprinting using a mobile phone is perhaps the most widely used method. However, this is power-hungry, and its typical positioning accuracy (2.0 to 3.0 meters) is not enough to differentiate between adjacent narrow aisles to locate items. In this paper, we present a novel (Bluetooth Low Energy) BLE Received Signal Strength Indication (RSSI) ranking based fingerprinting method that uses Kendall Tau Correlation Coefficient (KTCC) to correlate a new signal position with the signal strength ranking of multiple low-power iBeacon devices situated in a retail space. This offers a higher positioning accuracy and is supported in recent smartphones. An important source of error is the RSSI, which varies depending on the model and orientation of the phone. We present a novel way to mitigate this. We validated our method in a retail-like indoor space of Queen Mary Library. We achieved an average positioning error of 0.87 meters which was sufficient to differentiate between physical space aisles.