Adaptive Kalman filters for manoeuvring target tracking

Abstract

Two different adaptive Kalman filter designs, for tracking targets expected to perform varying turn manoeuvres, are presented. In the first one, the process noise covariance level of a second order Kalman filter is adjusted at each time step according to the estimated turn rate. The turning rate is estimated from the magnitude of the calculated acceleration divided by the estimated speed of the target. At each scan the previous and current velocity estimates are used to calculate the acceleration. The second filter uses a scale factor, representing the target unpredictability, which is estimated from the available data after a measurement is taken. The estimated scale factor is then used in the filter in the next scan. The comparison of the performance of the proposed algorithms is made with that of an IMM algorithm, employing three models with different levels of process noise covariance and also to that of a second order Kalman filter. Two different assumptions have been made for selecting the process noise values for the the IMM and Kalman filter algorithms, in the first case it was assumed that there was no prior information about the target motion whereas in the second case it was assumed that the largest turn rate that the target of interest could perform was known. The IMM algorithm utilizing three models gives slightly better estimates during the nonmanoeuvring periods, but the proposed algorithms are superior to the IMM algorithm in terms of estimation errors during manoeuvring periods.