No abstract

SUMMARYThree different algorithms based on the multimodel partitioning approach are designed for the problem of tracking a manoeuvering target utilizing aircraft derived data (ADD) measurements of heading. These are subsequently evaluated with respect to their performance in the sense of the mean square estimation error by means of simulations in three realistic tracking scenarios. The extend Kalman filter (EKF) for this problem has been used as a measure of comparison for the performance of all algorithms. The results indicate the improvement in performance obtained when using adaptive techniques to track manoeuvering targets instead of the non-adaptive EKF.

Three different algorithms based on the multimodel partitioning approach are designed for the problem of tracking a manoeuvering target utilizing aircraft derived data (ADD) measurements of heading. These are subsequently evaluated with respect to their performance in the sense of the mean square estimation error by means of simulations in three realistic tracking scenarios. The extend Kalman filter (EKF) for this problem has been used as a measure of comparison for the performance of all algorithms. The results indicate the improvement in performance obtained when using adaptive techniques to track manoeuvering targets instead of the non-adaptive EKF.