用于高分辨率大斜视SAR成像的广义非线性线性调频缩放算法

Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging.

作者信息

Yi Tianzhu, He Zhihua, He Feng, Dong Zhen, Wu Manqing

机构信息

School of Electronic Science and Engineering, National University of Defense Technology, Sanyi Avenue, Changsha 410073, China.

China Electronics Technology Group Corporation (CETC), China Academy of Electronics and Information Technology, Beijing 100846, China.

出版信息

Sensors (Basel). 2017 Nov 7;17(11):2568. doi: 10.3390/s17112568.

DOI:10.3390/s17112568

PMID:29112151

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5712855/

Abstract

This paper presents a modified approach for high-resolution, highly squint synthetic aperture radar (SAR) data processing. Several nonlinear chirp scaling (NLCS) algorithms have been proposed to solve the azimuth variance of the frequency modulation rates that are caused by the linear range walk correction (LRWC). However, the azimuth depth of focusing (ADOF) is not handled well by these algorithms. The generalized nonlinear chirp scaling (GNLCS) algorithm that is proposed in this paper uses the method of series reverse (MSR) to improve the ADOF and focusing precision. It also introduces a high order processing kernel to avoid the range block processing. Simulation results show that the GNLCS algorithm can enlarge the ADOF and focusing precision for high-resolution highly squint SAR data.

摘要

本文提出了一种用于高分辨率、大斜视合成孔径雷达（SAR）数据处理的改进方法。为解决由线性距离走动校正（LRWC）引起的调频速率方位向变化，人们提出了几种非线性调频变标（NLCS）算法。然而，这些算法对方位向聚焦深度（ADOF）的处理效果不佳。本文提出的广义非线性调频变标（GNLCS）算法采用级数反演法（MSR）来提高方位向聚焦深度和聚焦精度。它还引入了高阶处理核以避免距离分块处理。仿真结果表明，GNLCS算法能够增大高分辨率大斜视SAR数据的方位向聚焦深度和聚焦精度。