Model-set adaptation using a fuzzy Kalman filter

In this paper, a fuzzy Kalman filter is proposed to combat the model-set adaptation problem since it is found to be able to extract more exactly dynamic information. The fuzzy Kalman filter uses a set of fuzzy rules to adaptively control the noise covariance and that makes it more suitable for real radar tracking. The proposed fuzzy Kalman filter is then combined with an 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 target tracking data. Simulation result shows that the FIMM algorithm outperforms the AIMM algorithm in terms of both the root mean square prediction error and the number of track loss.