Model-set adaptation using a fuzzy Kalman filter

In this paper, a fuzzy Kalman filter (KF) is proposed to combat the model-s et adaptation problem of multiple model estimation. The fuzzy KF is found t o be able to more exactly extract dynamic information of target maneuvers. It uses a set of fuzzy rules to adaptively control the process noise covari ance of the KF and that makes it more suitable for real radar tracking. The proposed fuzzy Kalman filter is then incorporated into an interacting mult iple model (IMM) algorithm, hence, a fuzzy IMM (FIMM) algorithm is obtained . The performance of the FIMM algorithm is compared with that of an adaptiv e 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. (C) 200 1 Elsevier Science Ltd. All rights reserved.