Estimation of Extended Targets Based on Compressed Sensing in Cognitive Radar System

In this paper, the ranges and velocities of multiple extended targets are estimated by exploiting the target sparsity in the cognitive radar system. Different from the point targets in the traditional compressed sensing (CS) radar, the parameters of extended targets are expressed and estimated by using a novel CS-based model. Since the echo signals from extended targets are the convolutions between the transmitted waveform and target impulse responses (TIRs), the dictionary matrices in the proposed cognitive radar for all extended targets must be first established in the CS-based reconstruction algorithm. Then, the target parameters are estimated by reconstructing the nonzero entries of a sparse vector. To further improve the performance of CS reconst-ruction, a novel two-step method is proposed to minimize the mutual coherence of the dictionary matrix by optimizing the transmitted waveform. Simulation results demonstrate that the estimation performance of the extended targets is significantly improved by optimizing the transmitted waveform.