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

Abstract

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 /spl alpha/-/spl beta/ 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 /spl alpha/ and /spl beta/. 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.