An adaptive Kalman filter with sequential rescaling of process noise

An approach to adaptive Kalman filtering is presented. The filter utilizes a scale factor, which represents the target unpredictability at any time, as estimated from the available data. The performance of the algorithm is compared with that of an IMM algorithm and also with that of a standard Kalman filter. The proposed filter does not rely on a priori knowledge about the target motion and it produces better estimates than the IMM algorithm during manoeuvring periods.