Super-resolution ISAR Imaging via Statistical Compressive Sensing

摘要：

Developing compressed sensing (CS) theory has been applied in radar imaging by exploiting the inherent sparsity of radar signal. In this paper, we develop a super resolution (SR) algorithm for formatting inverse synthetic aperture radar (ISAR) image with limited pulses. Assuming that the target scattering field follows an identical Laplace probability distribution, the approach converts the SR imaging into a sparsity-driven optimization in Bayesian statistics sense. We also show that improved performance is achieved by taking advantage of the meaningful spatial structure of the scattering field. To well discriminate scattering centers from noise, we use the non-identical Laplace distribution with small scale on signal components and large on noise. A local maximum likelihood estimator combining with bandwidth extrapolation technique is developed to estimate the statistical parameters. Experimental results present advantages of the proposal over conventional imaging methods.

关键词： Inverse synthetic aperture radar (ISAR) super resolution (SR) compressive sensing (CS) Bayesian non-identical distribution.

作者: Shun-jun Wu Lei Zhang Meng-dao Xing

作者单位: National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China

会议类型: 国际会议

会议名称: 2011 IEEE CIE International Conference on Radar(2011年IEEE国际雷达会议RADAR 2011)

会议地点: 成都

会议语种:英文

页码: 545-550

在线出版日期: 2011-10-24（万方平台首次上网日期，不代表论文的发表时间）