Compressive sensing aided the sequential extended Kalman filter tracker for pulse Doppler radar

Since target tracking using pulse Doppler (PD) radar plays a significant role in military applications and the traditional tracking methods are not perfect enough to complete the tracking filter process, compressive sensing (CS) is applied for improving the tracking precision. In this paper, CS aided sequential extended Kalman filter (SEKF) is proposed to track moving targets using pulse Doppler (PD) radar. We use the sparsity of delay-Doppler plane to set up a sparse signal model in each pulse interval, and get Doppler measurements through the reconstruction algorithm. Then SEKF is used to make filter update so as to attain the high-precision state estimation. In the process of filter, we use SEKF to reduce the nonlinearity between Doppler measurements and the target motion state. This method can not only take advantages of CS, but also decrease the nonlinear error through adding the pseudo, which can improve the tracking accuracy of PD radar more significantly. Numerical simulations show that our proposed algorithm is validated to enhance the tracking performance compared to the traditional SEKF method and the CS based tracking method. In addition, parameters used in this paper are more practical and of great significance and value.