Radar tracking for air surveillance in a stressful environment using a fuzzy-gain filter

We present a fuzzy-gain filter for target tracking in a stressful environment where a target may accelerate at nonuniform rates and may also complete sharp turns within a short time period. Furthermore, the target may be missing from successive scans even during the turns, and its positions may be detected erroneously. The proposed tracker incorporates fuzzy logic in a conventional α-β filter by the use of a set of fuzzy if-then rules. Given the error and change of error in the last prediction, these rules are used to determine the magnitude of α and β. The proposed tracker has the advantage that it does not require any assumption of statistical models of process and measurement noise and of target dynamics. Furthermore, it does not need a maneuver detector even when tracking maneuvering targets. The performance of the fuzzy tracker is evaluated using real radar tracking data generated from F-18 and other fighters, collected jointly by the defense departments of Canada and the United States. When compared against that of a conventional tracking algorithm based on a two-stage Kalman filter, its performance is found to be better both in terms of prediction accuracy and the ability to minimize the number of track losses