崔磊^1,,,
仇晓兰^{1, 2, 3,,},
郭嘉逸^{2, 3},
温雪娇¹,
杨俊莹^{2, 3},
付琨^{2, 3}
1.中国科学院电子学研究所苏州研究院 苏州 215123
2.中国科学院空天信息创新研究院 北京 100190
3.微波成像技术国家级重点实验室 北京 100190
基金项目:国家自然科学基金(61991420, 61991421)

详细信息

作者简介:崔磊：崔　磊(1989–)，男，甘肃天水人，2016年在北京理工大学信息与电子学院获得硕士学位，中国科学院电子学研究所苏州研究院助理研究员。主要研究领域为SAR数据预处理。E-mail: cuilei1167@163.com
仇晓兰(1982–)，女，中国科学院空天信息创新研究院研究员，博士生导师，主要研究领域为SAR成像处理、SAR图像理解，IEEE高级会员、IEEE地球科学与遥感快报副主编、雷达学报青年编委。E-mail: xlqiu@mail.ie.ac.cn

通讯作者:崔磊 cuilei1167@163.com
仇晓兰 xlqiu@mail.ie.ac.cn

责任主编：陈杰 Corresponding Editor: CHEN Jie
中图分类号:TN957.52

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被引次数:0

出版历程

收稿日期:2020-07-07
修回日期:2020-09-04
网络出版日期:2020-09-28

Multi-channel Phase Error Estimation Method Based on an Error Backpropagation Algorithm for a Multichannel SAR

CUI Lei^1,,,
QIU Xiaolan^{1, 2, 3,,},
GUO Jiayi^{2, 3},
WEN Xuejiao¹,
YANG Junying^{2, 3},
FU Kun^{2, 3}
1. Institute of Electronics, Chinese Academy of Sciences, Suzhou Research Center, Suzhou 215123, China
2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
3. National Key Lab of Microwave Imaging Technology, Beijing 100190, China
Funds:The National Natural Science Foundation of China (61991420, 61991421)

More Information

Corresponding author:CUI lei, cuilei1167@163.com;QIU Xiaolan, xlqiu@mail.ie.ac.cn

摘要
摘要:方位向多通道合成孔径雷达(SAR)可实现高分辨率宽测绘带成像，准确估计通道间相位误差是保障成像质量的关键。该文提出了基于误差反向传播训练优化的通道相位误差估计方法，该方法根据多通道SAR回波生成的物理过程，构建含有通道间相位误差待估计参数的观测矩阵，通过初始化的通道误差和初始化的目标散射系数参数生成初始化的SAR回波，并计算该回波与多通道SAR实测回波之间的误差，通过深度学习中常用的误差反向传播的方法，不断训练优化上述参数，最终获得通道间相位误差的估计值，同时也得到了对稀疏目标散射系数的估计。该方法基于误差反向传播方法，并将该方法与通道误差的形成原理相结合，在稀疏假设下同时完成了相位估计和成像，为多通道SAR误差估计提供了一种全新的思路。多通道SAR仿真数据验证了该文算法的有效性。
关键词:合成孔径雷达/
高分辨率宽测绘带/
误差反向传播/
多通道SAR/
相位误差估计
Abstract:An azimuth multi-channel Synthetic Aperture Radar (SAR) can be used to obtain high-resolution wide-swath SAR images. Accurate estimation of the phase error between channels is the key to ensuring image quality. In this study, we present a channel phase error estimation method based on the error backpropagation algorithm. During the physical process of a multi-channel SAR echo generation, this method constructs an observation matrix with the parameters to be estimated including the phase error between channels. The initial SAR echo is generated using the initial channel error matrix and initial target scattering coefficient matrix, and the error between the echo and measured multi-channel SAR echo is calculated. Using the backpropagation algorithm commonly used in deep learning, the abovementioned parameters are continuously trained and optimized. Finally, the estimation of the phase error between channels is obtained along with the target scattering coefficient. This method combines the error backpropagation method with the principle of multi-channel SAR channel error. Phase estimation and imaging are realized based on the sparsity assumption, which provides a new approach for estimating an error in a multi-channel SAR. The effectiveness of the presented method is validated using multi-channel SAR simulation data.
Key words:Synthetic Aperture Radar (SAR)/
High-resolution wide-swath/
Error backpropagation algorithm/
Multi-channel SAR/
Phase error estimation



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