Robustly blind sparsity signal recovery algorithm for compressive sensing radar

Compressive sensing (CS) is an emerging data acquisition method under the condition that the signal is sparse or compressible. However, applying CS in radar to reconstruct target scene always requires the sparsity of the echo signal is known priori with high Signal to Interference and Noise Ratio (SINR). Such an ideal assumption may not be met in practical situations. In this paper, a robustly blind sparsity recovery algorithm for compressive sensing radar (CSR) is presented. The proposed method could enhance the performance of targets detection and range-Doppler parameters estimation in low SINR without known the sparsity of the original signal with the idea of choosing supplements of the sparse signal adaptively and optimizing transmit waveform. The numerical simulations are carried out to verify the effectiveness of the proposed method.