A sparsity based approach to velocity SAR imaging

R. Raj, R. Jansen, M. Sletten
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引用次数: 0

Abstract

We recently successfully developed an airborne MSAR (Multichannel Synthetic Aperture Radar) test bed system that consists of 32 along-track phase centers through the use of two transmit horns and 16 receive antennas [1-4]. We have subsequently deployed this system, both in September 2014 and more recently in October 2015, to perform extensive and systematic data collections on a variety of land-based and maritime targets under different environmental conditions. The resulting data poses important signal processing challenges pertaining to optimum ways of combining the signals obtained from various channels so that the underlying information of interest can be effectively extracted in the presence of noise and clutter. In this paper we focus on the imaging problem and propose a novel method of simultaneously exploiting the multichannel structure of the data acquisition and the underlying sparse structure of the scene being imaged. After giving a brief overview of our airborne NRL MSAR system and the basics of velocity processing, we proceed to describe our novel algorithm and demonstrate our initial experimental results. The novelty of this paper is two-fold: to the best of our knowledge, this is first time that velocity processing has been used in conjunction with sparsity based processing; and that the resulting approach is applied to real data captured by our airborne NRL MSAR system.
基于稀疏度的速度SAR成像方法
我们最近成功开发了机载多通道合成孔径雷达(MSAR)测试平台系统,该系统通过使用两个发射喇叭和16个接收天线,由32个沿轨道相位中心组成[1-4]。随后,我们在2014年9月和2015年10月部署了该系统,在不同环境条件下对各种陆基和海上目标进行了广泛而系统的数据收集。由此产生的数据提出了重要的信号处理挑战,涉及到组合从各种信道获得的信号的最佳方法,以便在存在噪声和杂波的情况下有效地提取感兴趣的潜在信息。本文针对成像问题,提出了一种同时利用数据采集的多通道结构和被成像场景的底层稀疏结构的新方法。在简要介绍了我们的机载NRL MSAR系统和速度处理的基础知识后,我们继续描述我们的新算法并演示我们的初步实验结果。本文的新颖之处在于两个方面:据我们所知,这是第一次将速度处理与基于稀疏性的处理结合使用;并将所得到的方法应用于我们机载NRL MSAR系统捕获的实际数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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