A Fast Compressed Sensing 3D SAR Imaging Method Based on the Adaptive Threshold

Aiming at improving the computational efficiency of compressed sensing (CS) algorithms for synthetic aperture radar (SAR) three dimensional (3D) imaging, a fast compressed sensing imaging algorithm based on adaptive threshold (FCSIAT) is proposed in this paper. Firstly, SAR echoes after range compression are considered as the cluster samples, and classified into several subclasses by the fuzzy c-means clustering algorithm. Secondly, in order to extract the possible targets echoes as completely as possible, the extracting threshold of echoes is generated by the two subclasses echoes with the smallest amplitude in the classification results automatically. Finally, echoes with higher amplitude than the extracting threshold are extracted for SAR 3D sparse imaging. Compared with traditional CS algorithms, FCSIAT algorithm has translated the 3D imaging with whole echoes into imaging processing according to the possible targets echoes, and reduces the computational complexity effectively. Meanwhile, FCSIAT algorithm improves the imaging quality by eliminating the echo signal of signal noise and false targets. Simulation and experimental results has proved the effectiveness of FCSIAT algorithm for SAR 3D imaging.