New Sequential Extended Kalman Filter for Pulse Doppler Radar Tracker Based on Compressive Sensing

A new sequential extended Kalman filter (SEKF) based on compressive sensing (CS) is proposed to track maneuvering targets for the pulse Doppler radar. The sparsity of target measurements in delay-Doppler plane is used to set up a sparse signal model in each pulse interval, and then Doppler measurements can be obtained through the reconstruction algorithm. Finally, SEKF is used to make filter update so as to attain the highprecision state estimation. In the process flow of tracking filter, CS processing is applied to improve estimate accuracy of delay and Doppler for targets, and SEKF is applied to reduce the nonlinearity between Doppler measurements and target motion state through adding the pseudo. Simulation results show that compared to the traditional SEKF method and the existing CS based tracking method, CS aided SEKF method has the highest tracking accuracy. Therefore, this proposed algorithm is validated to enhance the tracking performance of maneuvering target.