压缩感知ISAR成像的全变差优化最小算法

ISAR compressive imaging based on Majorization-Minimization of total variation

对于逆合成孔径雷达(ISAR)目标成像,从少量压缩测量回波数据重建高分辨率运动目标是不适定问题,且观测噪声也会影响重建结果。在频率步进连续波ISAR系统回波观测模型基础上,结合压缩感知原理,给出了一种基于全变差正则化的ISAR压缩感知成像模型,通过将该优化模型转化为一系列简单代理函数进行求解,提出了一种快速优化最小算法。最后在不同回波信噪比条件下进行仿真验证。实验结果表明,当回波信噪比大于10 dB时,本文方法明显优于距离–多普勒算法和基于L1范数的压缩感知成像方法。

Reconstruction of Inverse Synthetic Aperture Radar（ISAR） image from its limited number of compressive echo samples is an ill-posed problem and the quality of final image significantly depends on the noise level. In this paper,a total variation based variational model is proposed for ISAR imaging from finite number of compressive echo samples based on ISAR system signal model with stepped frequency continuous wave and compressive sensing theory. An efficient Majorization-Minimization（MM） algorithm is also developed to seek the solution of the proposed model by minimizing a sequence of quadratic surrogate penalties. Results of simulated experiments with various noise levels demonstrate that the proposed method outperforms Range-Doppler（RD） algorithm and L1 norm based method when echo Signal-to-Noise Ratio（SNR） is above 10 dB.