volume | PIER Journals

Abstract

Compressed sensing (CS) has attracted significant attention in the radar community. The better understanding of CS theory has led to substantial improvements over existing methods in CS radar. But there are also some challenges that should be resolved in order to benefit the most from CS radar, such as radar signal with low signal to noise ratio (Low-SNR). In this paper, we will focuses on monostatic chaotic multiple-input-multiple-output (MIMO) radar systems and analyze theoretically and numerically the performance of sparsity-exploiting algorithms for the parameter estimation of targets at Low-SNR. The novelty of this paper is that it capitalizes on chaotic coded waveform to construct measurement operator incoherent with noise and singular value decomposition (SVD) to suppress noise. In order to improve the robustness of azimuth estimation, interpolation method is applied to construction of sparse bases. The gradient pursuit (GP) algorithm for reconstruction is implemented at Low-SNR. Finally, the conclusions are all demonstrated by simulation experiments.

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Prof. Meng Yang

Dr. Gong Zhang