提出一种应用于毫米波稀疏阵列成像的基于频率域成像算法和压缩感知技术相结合的成像算法。算法包含两个主要步骤，首先采用等效相位中心近似原理，将快速傅里叶变换成像算法用于周边形阵列，由于等效相位中心近似引入的残余相位误差无法在近距离成像应用中被完全补偿，因此在第二个步骤中，提出基于压缩感知技术的基于迭代最小化的稀疏学习（SLIM）的改进算法用于重聚焦初始图像。通过等效相位中心近似原理和改进的SLIM算法的结合，所提算法具备更高的计算效率、提升了图像质量、相比于传统的SLIM算法具备更少的迭代次数。仿真结果验证了所提算法的有效性。

An algorithm combining frequency domain imaging algorithm and compressed sensing （CS） framework is proposed in here for millimeter-wave multi-static sparse array imaging. The algorithm consists of two major steps. Firstly, the typical fast Fourier transform （FFT） algorithm used in square boundary array with phase center approximation （PCA） is carried out. However, the residual phase error introduced by the PCA at close range cannot be compensated completely, so in the second step, the modified sparse learning via iterative minimization （SLIM） algorithm which is in the CS framework is introduced to refocus the initial images. By combining PCA and the modified SLIM algorithm, the proposed algorithm reaches a better computational efficiency, improves the image quality, and alleviates the requirement for iterations of the original SLIM algorithm. Simulation results verify the effectiveness of this algorithm.