Indoor localization and tracking by multi sensor fusion in Kalman filter

Abstract

Indoor localization and tracking algorithm is an important research area used in various applications. Received signal strength indicator (RSSI) that can be measured in wireless sensor networks has low accuracy due to the reflections and obstacles. Sensors that have accumulating errors such as accelerometers, estimate position in short durations. Fusion of RSSI and accelerometer values in Kalman Filter yields more accurate results in localization and tracking. In this paper, position estimation is done with the fuse of RSSI measurements taken in wireless sensor network consisting of three beacon nodes, one mobile node and accelerometer measurements in Kalman filter. Also, a method that minimizes error in position estimation is demonstrated on empirical data.