基于2D-SOONE算法的MIMO稀疏面阵二维成像

为了解决采用传统的1D-CS算法进行分维处理时丢失耦合信息导致越单元徙动、影响成像质量且运算时间长的问题，研究了接收阵元整行整列稀疏的MIMO面阵结构特性，分析了该稀疏面阵所接收回波信号的二维联合稀疏特性，采用2D-SOONE算法对回波信号进行二维联合重构，算法采用序列一阶负指数取代传统SL0算法的高斯函数，拓至二维并利用梯度投影求解，具有二维联合重构性能的同时提高重构精度。通过实验，仿真了该算法在不同阵列稀疏度、不同信噪比下用于MIMO稀疏面阵的成像效果。仿真结果表明，2D-SOONE抑制了传统的1D-CS算法的越单元徙动问题，减少了运算时间，且成像质量较2D-SL0更优。

Sparse MIMO Planar Array 2D Imaging Based on 2D SOONE Algorithm

Sparse recovery algorithm can realize sparse MIMO planar array two dimensional imaging. When the traditional 1D-CS algorithm is adopted to treat with the dimension sorting in processing, there will be a loss of the coupling information, the migration of cell will be caused by, the image is poor in quality, and time is too long in operation. For the reason mentioned above, the structure characteristics of MIMO planar array are studied in this paper. The paper analyzes the joint sparse feature of the two dimensional data accepted by MIMO and realizes the joint reconstruction of the two dimensional data by adopt 2D-SOONE algorithm. The algorithm uses sequential order one negative exponential function instead of Gaussian function of the traditional SL0 algorithm, extends to the two dimensions and solved by gradient projection, and has the performance of the two dimensional joint reconstruction, and the precision of reconstruction is improved. Through experiments, the imaging effect of the algorithm for MIMO sparse array is simulated under different array sparsity and SNR. The simulation results show that the 2D-SOONE algorithm suppresses the cell migration problem of the traditional 1D CS algorithm, and reduces the operation time. The imaging quality is better than that of the 2D-SL0 algorithm.