An improved prediction based strategy for target tracking in wireless sensor networks

The indoor localisation of moving target in wireless sensor networks using received signal strength indicator RSSI is addressed in this paper. A novel location tracking algorithm which combines an ensemble learning method and Kalman filter is proposed. An ensemble-based regression tree using received signal strength method has been proposed to localise static sensor nodes. In this paper, this approach is employed to solve the complex relation between the RSSI behaviour and the target position. Then, the estimated location is introduced in the Kalman filter as the observed information, leading to more accurate state of the moving target. Experimental results show that the adopted solution achieves a high accuracy compared to localisation algorithms currently available in the literature.