A fast compressed sensing algorithm via the Otsu algorithm for LASAR 3D sparse imaging

Abstract

Aiming at improving the computational efficiency of compressed sensing (CS) algorithms for linear array synthetic aperture radar (LASAR) three dimensional (3D) imaging, a fast compressed sensing imaging algorithm via the Otsu algorithm (FCSIO) is proposed in this paper. Firstly, the Grayscale value and initial Grayscale thresholds of LASAR echo signals are obtained to classify the echo signals into echo signals of targets and background echo signals. Secondly, the variance between targets and background's echo signals under every Grayscale threshold is calculated to quantify the difference between targets and background's echo signals, the optimal Grayscale threshold and classification results are obtained under the maximum variance. Finally, the possible targets echo signals are extracted for 3D high-quality imaging by the optimal Grayscale threshold and the Grayscale value of echo signals. Compared with traditional CS algorithms, FCSIO 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, FCSIO algorithm improves the imaging quality by eliminating the influence of signal noise and false targets. Simulation and experimental results prove the effectiveness of FCSIO algorithm for LASAR 3D sparse imaging.