Model-set adaptation using a fuzzy Kalman filter

In this paper, a fuzzy Kalman filter (KF) is proposed to combat the model-set adaptation problem of multiple model estimation. The fuzzy KF is found to be able to more exactly extract dynamic information of target maneuvers. It uses a set of fuzzy rules to adaptively control the process noise covariance of the KF and that makes it more suitable for real radar tracking. The proposed fuzzy Kalman filter is then incorporated into an interacting multiple model (IMM) algorithm, hence, a fuzzy IMM (FIMM) algorithm is obtained. The performance of the FIMM algorithm is compared with that of an adaptive IMM (AIMM) algorithm using real radar data. Simulation result shows that the FIMM algorithm greatly outperforms the AIMM algorithm in terms of both the root mean square prediction error and the number of track loss.