MAKF algorithm and its application in radar data processing

The memory attenuated Kalman filter (MAKF) algorithm is introduced for radar tracking and measurement of high dynamic target's range and velocity, and a new determination method of memory attenuated factor which can judge and repress the filter divergence is proposed. The algorithm increases the weight of measurement in state estimation, so the biases of range and velocity caused by acceleration can be eliminated, and the filter divergence caused by high dynamic target can be repressed. The simulation results showed that the performance of the proposed algorithm and the standard Kalman filter (KF) algorithm are similar in low dynamic situation, while the systematic biases and the random errors of the proposed algorithm are significantly smaller than the standard KF algorithm in high dynamic situations. The proposed algorithm can also repress the filter divergence caused by the general accelerated motion.