A New Method for Ground Moving Targets Tracking Using Radar Based on Compressed Sensing

In this paper, we propose a Compressed Sensing (CS) based method under the unknown sparse degree to track ground moving targets using Pulse-Doppler (PD) radar. We use the sparsity of delay-Doppler plane in the process of disposing PD radar echo to set up a sparse signal model in each pulse interval. At the state prediction stage, we can get the predicted values of target states by dynamic equations, with which we can build a delay-Doppler grid that is used to form orthogonal dictionary. At the state update stage, we can get the target state estimation through reconstruction algorithm, so as to realize precise tracking of targets. The problem of target tracking by PD radar will be transformed into the reconstruction of the sparse signal, which is accomplished by getting the location of targets in the grid, as a result of achieving ground target tracking based on Orthogonal Matching Pursuit (OMP) [1]. Then, aiming at the sparsity problem in the method of target tracking based on Orthogonal Matching Pursuit, we propose a new target tracking method based on Sparsity Adaptive Matching Pursuit (SAMP) algorithm [2]. Numerical simulations show that our tracking method can not only provide the equivalent computational time, but also get better tracking performance than the KF-based tracking.