245 / 2020-01-04 03:27:00

Segmented Random Sparse MIMO-SAR 3-D Imaging Based On Compressed Sensing

MIMO-SAR; Segmented random sparse; CS; UGV; Sparsity

IEEE SAM 2020: Full Regular Papers > Multi-channel imaging and hyperspectral processing

This paper presents a segmented random sparse multiple-input and multiple-output synthetic aperture radar (MIMO-SAR) 3-D imaging based on compress sensing (CS). Since the targets of interest for unmanned ground vehicle (UGV) forward looking array are usually sparse, the system complexity can be reduced using CS theory. The sparsity is determined by 2-D images of different positions during UGV moving forward, which can reduce the reconstruction time without multiple iterations. Combining the MIMO array and the path of UGV, 3-D imaging of the forward scene can be achieved. Segmented random sparse of the original data from MIMO-SAR ensure the accuracy of 3-D reconstruction, by using sufficient information. Simulation results are presented to demonstrate the validity of the proposed method.