Improving the Accuracy of Bluetooth Low Energy Indoor Positioning System Using Kalman Filtering

Abstract

Indoor positioning systems (IPS) are an emerging area of research that can be enabled by a variety of wireless technologies powered by the Internet of Things (IoT). In particular, the introduction of Bluetooth Low Energy (BLE) beacons has been a major catalyst for much of the recent interest in IPS. This paper focuses on the implementation and development of a simple IPS Android application using BLE beacons and the application of Kalman filtering for improved accuracy. The IPS Android application uses the filtered received signal strength indication (RSSI) to approximate the location of an Android device as it moves throughout a location that has been outfitted with BLE beacons. The location data that is generated by the application is sent to a database to allow for previous location history to be examined and to enable to application to watch the movements of tracked devices in real time.