双基地角时变下的ISAR稀疏孔径自聚焦成像

朱晓秀; 胡文华; 马俊涛; 郭宝锋; 薛东方

doi:10.7527/S1000-6893.2018.22059

航空学报 >

2018 , Vol. 39 >Issue 8: 322059 - 322059

DOI: https://doi.org/10.7527/S1000-6893.2018.22059

电子电气工程与控制

双基地角时变下的ISAR稀疏孔径自聚焦成像

朱晓秀 ,
胡文华 ,
马俊涛 ,
郭宝锋 ,
薛东方

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陆军工程大学(石家庄校区) 电子与光学工程系, 石家庄 050003

收稿日期: 2018-01-29

修回日期: 2018-05-07

网络出版日期: 2018-05-07

基金资助

国家自然科学基金（61601496）

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ISAR autofocusing imaging with sparse apertures and time-varying bistatic angle

ZHU Xiaoxiu ,
HU Wenhua ,
MA Juntao ,
GUO Baofeng ,
XUE Dongfang

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Department of Electronic and Optical Engineering, Army Engineering University(Shijiazhuang Campus), Shijiazhuang 050003, China

Received date: 2018-01-29

Revised date: 2018-05-07

Online published: 2018-05-07

Supported by

Science Foundation of China (61601496)

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摘要

针对双基地角时变下的逆合成孔径雷达（ISAR）成像分辨率低以及稀疏孔径存在相位误差引起图像散焦等问题，提出了一种基于贝叶斯压缩感知（BCS）的双基地ISAR稀疏孔径自聚焦高分辨成像算法。在平动补偿后回波数据的基础上，首先构造补偿相位将由双基地角时变引起的多普勒偏移补偿掉，然后构造随双基地角变化的稀疏基矩阵，建立基于压缩感知的双基地ISAR稀疏孔径观测模型，并将相位误差作为ISAR成像的模型误差，接着假设目标图像各像元服从Laplace先验、噪声统计特性服从Gaussian分布，利用贝叶斯推理进行"分布式"迭代求解，在高分辨成像的同时实现了相位自聚焦，仿真结果验证了算法的有效性和优越性。

关键词： 双基地逆合成孔径雷达; 双基地角时变; 稀疏孔径; 自聚焦; 贝叶斯压缩感知; Laplace先验

本文引用格式

朱晓秀 , 胡文华 , 马俊涛 , 郭宝锋 , 薛东方 . 双基地角时变下的ISAR稀疏孔径自聚焦成像[J]. 航空学报, 2018 , 39(8) : 322059 -322059 . DOI: 10.7527/S1000-6893.2018.22059

Abstract

To solve the problems of poor resolution of bistatic Inverse Synthetic Aperture Radar (ISAR) imaging with time-varying bistatic angle and image defocus caused by the space-varying phase error in traditional sparse aperture imaging, a high-resolution imaging algorithm integrated with phase error correction is proposed based on Bayesian Compressive Sensing (BCS). First, based on the echo data which have been compensated after motion compensation, a phase compensation term is constructed to compensate the Doppler shift caused by the time-varying bistatic angle. Second, a sparse basis matrix changing with the time-varying bistatic angle is constructed to establish a model for compressive sensing-based bistatic ISAR imaging with sparse apertures. The phase error is then treated as the modeling error in ISAR imaging. It is then assumed that each pixel of the target image follows a Laplace prior and noise follows Gaussian prior. Bayesian inference is used to realize non-ambiguous azimuth imaging integrated with phase error correction iteratively. The simulation results verify the validity and superiority of the proposed algorithm.

Key words： bistatic inverse synthetic aperture radar; time-varying bistatic angle; sparse aperture; autofocusing; Bayesian compressive sensing; Laplace prior

参考文献

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