Kalman Filter Fusion Based on Interactive Multiple Model for Target Tracking in Wireless Sensor Networks

Target tracking in wireless sensor networks has become one of the most challenging and popular topics in the last decade. Target tracking includes different algorithms, one of the most effective of which is the interactive multiple model algorithm based on the Kalman filter (IMM-KF). This algorithm is used when the target is maneuverable and the equations of motion are linear. In this paper, in order to have a better tracking performance and obtain extensive information from the target in wireless sensor networks, a fusion algorithm based on Kalman filter and interactive multiple model is proposed. Finally, the Monte Carlo simulation results of the proposed fusion algorithm are compared with single-sensor algorithms.