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一种基于合成孔径雷达海洋图像中多普勒频谱的方位角天线方向图估计方法。

An Azimuth Antenna Pattern Estimation Method Based on Doppler Spectrum in SAR Ocean Images.

作者信息

Meng Hui, Wang Xiaoqing, Chong Jinsong

机构信息

National Key Laboratory of Science and Technology on Microwave Imaging, Beijing 100190, China.

Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China.

出版信息

Sensors (Basel). 2018 Apr 3;18(4):1081. doi: 10.3390/s18041081.

DOI:10.3390/s18041081
PMID:29614058
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5948649/
Abstract

In synthetic aperture radar (SAR) ocean remote sensing, it is very difficult to estimate an accurate azimuth antenna pattern (AAP) from low-scattering SAR images without strong scattering targets. Therefore, an azimuth antenna pattern estimation method based on Doppler spectrum in SAR ocean images is proposed in this paper. In order to preserve the complete AAP information, an azimuth unweighted matched filter is used to re-image the SAR raw data in the proposed method. Then, the shape factor of AAP can be obtained by linear statistics of the relationship between Doppler center and edge frequency spectrum in Doppler spectrum of each distance gate. In addition, the impact of the uniformity and signal-to-noise ratio of SAR ocean images on the estimation results are also analyzed by simulation. Finally, the feasibility of proposed method is verified by data from ERS-2 (European remote sensing satellite (ERS) was the European Space Agency's first Earth-observing satellite). Experimental results show that the AAP estimated by proposed method has a good estimation result.

摘要

在合成孔径雷达(SAR)海洋遥感中,从没有强散射目标的低散射SAR图像中估计精确的方位角天线方向图(AAP)非常困难。因此,本文提出了一种基于SAR海洋图像中多普勒频谱的方位角天线方向图估计方法。为了保留完整的AAP信息,在所提方法中使用方位角无加权匹配滤波器对SAR原始数据进行重成像。然后,通过对每个距离门的多普勒频谱中多普勒中心与边缘频谱之间关系的线性统计,可得到AAP的形状因子。此外,还通过仿真分析了SAR海洋图像的均匀性和信噪比对方位角天线方向图估计结果的影响。最后,利用欧洲遥感卫星2号(ERS - 2)(欧洲遥感卫星(ERS)是欧洲航天局的第一颗地球观测卫星)的数据验证了所提方法的可行性。实验结果表明,所提方法估计的方位角天线方向图具有良好的估计效果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/f5dc48c9a31a/sensors-18-01081-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/b18904d1ebe6/sensors-18-01081-g0A1a.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/de7aba7cc030/sensors-18-01081-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/f03533280293/sensors-18-01081-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/f413d351182c/sensors-18-01081-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/378ba7ab275b/sensors-18-01081-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/202d7da16185/sensors-18-01081-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/5359abe616cf/sensors-18-01081-g010a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/5afcf0610ea5/sensors-18-01081-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/f5dc48c9a31a/sensors-18-01081-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/b18904d1ebe6/sensors-18-01081-g0A1a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/73e06b40c69d/sensors-18-01081-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/cc7c6090293d/sensors-18-01081-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/e582530d7181/sensors-18-01081-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/1fea696a9aad/sensors-18-01081-g004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/f03533280293/sensors-18-01081-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/f413d351182c/sensors-18-01081-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/378ba7ab275b/sensors-18-01081-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/202d7da16185/sensors-18-01081-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/5359abe616cf/sensors-18-01081-g010a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/5afcf0610ea5/sensors-18-01081-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fbe/5948649/f5dc48c9a31a/sensors-18-01081-g012.jpg

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