ESTIMATION OF STRUCTURED COVARIANCE MATRICES FOR TOMOSAR FOCUSING

被引：0

作者：

Martin-del-Campo-Becerra, Gustavo Daniel ^{[1
]}

Torres-Garcia, Eduardo ^{[2
]}

Nannini, Matteo ^{[1
]}

Reigber, Andreas ^{[1
]}

Torres-Roman, Deni Librado ^{[2
]}

机构：

[1] German Aerosp Ctr DLR, Microwaves & Radar Inst, Oberpfaffenhofen, Germany

[2] Natl Polytech Inst IPN, Ctr Res & Adv Studies Cinvestav, Guadalajara, Mexico

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

Capon; Subspace Fitting; Synthetic Aperture Radar; Tomography;

D O I：

10.1109/IGARSS52108.2023.10282533

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Most common focusing techniques for Synthetic Aperture Radar (SAR) Tomography (TomoSAR), e.g. Matched Spatial Filtering and Capon, make use of the conventional sample covariance matrix, obtained from a finite number of observations. Yet, structured covariance matrix estimates can be employed in lieu of the sample covariance matrix. Accordingly, our simulation study shows that Capon's performance improves with the use of structured covariance matrices. These are obtained with the Subspace Fitting approach, properly adapted to TomoSAR. Numerical comparisons between structure and unstructured covariance matrices are presented.

引用

页码：7937 / 7940

页数：4

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