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In this paper, a fuzzy logic-based recursive least squares filter (FLRLSF) is presented for maneuvering target tracking (MTT) in situations of observations with unknown random characteristics. In the proposed filter, fuzzy logic is applied in the standard recursive least squares filter (RLSF) by the design of a set of fuzzy if-then rules. Given the observation residual and the heading change in the current prediction, these rules are used to determine the magnitude of the fading factor of RLSF. The proposed filter has an advantage in which the restrictive assumptions of statistical models for process noise, measurement noise, and motion models are relaxed. Moreover, it does not need a maneuver detector when tracking a maneuvering target. The performance of FLRLSF is evaluated by using a simulation and real test experiment, and it is found to be better than those of the traditional RLSF, the fuzzy adaptive alpha-beta filter (FA alpha-beta F), and the hybrid Kalman filter in tracking accuracy.

Maneuvering target tracking using fuzzy logic-based recursive least squares filter