Efficient 3-D Near-Field MIMO-SAR Imaging Based on Scanning MIMO Array

Multiple-Input-Multiple-Output (MIMO) synthetic aperture radar (SAR) (MIMO-SAR) has numerous potential applications in fields such as medical diagnosis and security screening. In near-field MIMO-SAR imaging, the virtual array is typically defined as the midpoint of the transceiver array. Based on the virtual array, the multistatic data can be converted to monostatic data for imaging. However, these approaches suffer from low accuracy and cannot reconstruct large scenes, despite their small computational load. In this paper, we propose a novel near-field MIMO-SAR imaging algorithm based on range compensation. The convolution between transmitting and receiving elements of the MIMO array defines the positions of virtual array's elements, with the center of the small scene selected as the reference point. The range difference between the transceiver array and the corresponding virtual array to the reference point is calculated. An approximate expression of the range difference is also derived, which can effectively reduce the computational load associated with the range compensation. The conditions for partitioning large scenes are derived by analyzing the range difference between the reference point and other scattered points with respect to the antenna elements. Based on the conditions, the large scenes are divided into blocks. The final image is obtained by a weighted sum of each block scene image. Simulation and experimental results on the designed MIMO radar near-field imaging system demonstrate that the proposed algorithm can effectively reconstruct high-resolution scene images of arbitrary size.