基于多观测向量块稀疏的MIMO雷达非理想正交波形成像

基于稀疏恢复的多输入多输出(multiple input multiple output, MIMO)雷达波形分离方法,能够代替匹配滤波,提高MIMO雷达非理想正交波形分离效果,对目标高分辨成像。但由于目标像稀疏性较弱,多观测向量(multiple measurement vector, MMV)稀疏恢复算法的效果有限。通过调整感知矩阵发掘目标像的块稀疏性,提出了一种基于块稀疏的MMV稀疏重构算法来提高成像质量。首先采用改进的复合三角函数(improved composite trigonometric function, ICTF)作为平滑函数近似l₀范数,然后将其扩展到基于块稀疏的MMV模型,最后通过自适应调整正则化参数提升算法稳健性。通过实验验证了该算法在不同稀疏度、不同信噪比下的重构性能,仿真分析了其应用于MIMO雷达对多散射点目标模型的成像效果。仿真结果表明,所提算法能够更好地提高成像质量。

Non-ideal orthogonal waveforms imaging of MIMO radar based on multiple measurement vector block sparse algorithm

The multiple input multiple output (MIMO) radar waveform separation method based on the sparse recovery can improve the quality of non-ideal orthogonal waveform separation of MIMO radar and obtain high resolution images, which can replace the matched filter. However, due to the weak sparseness of the target image, the effect of the multiple measure ment vector (MMV) sparse recovery algorithm is limited. By adjusting the perceptual matrix to explore the block sparsity of the target image, a block sparse based on MMV sparse reconstruction algorithm is proposed to improve the image quality. Firstly, the improved composite trigonometric function (ICTF) is used as a smoothing function to approximate the l₀ norm. Then, it is extended to the MMV model based on block sparse. Finally, the robustness of the algorithm is improved by the adaptive adjustment of regularization parameters. The reconstruction performance of the algorithm under different sparseness and signal to noise ratio (SNR) is verified by experiments. The imaging effect of the MIMO radar on the multiple-scattering points target model is analyzed. The simulation results show that the proposed algorithm can improve the imaging quality.