An Adaptive Kalman Filter For Tracking Maneuvering Targets

In this paper, an adaptive Kalman filter is presented. The proposed filter calculates the process noise covariance which determines the tracking ability of the Kalman filter at every update time. Thus, the filter becomes sensitive to variations in the the target motion. In the filter, process noise covariance is updated at every sampling interval according to a predetermined relationship between the innovation covariance of the Kalman filter and available data form the measurements. Then state estimation and state estimation covariance are updated using the new process noise covariance. Tracking performance of the proposed algorithm has been compared to the Interactive multiple model filter through simulations