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Resolution Enhancement of Spatial Parametric Methods via Regularization

Martin del Campo Becerra, Gustavo and Serafín García, Sergio Alejandro and Reigber, Andreas and Ortega Cisneros, Susana and Nannini, Matteo (2021) Resolution Enhancement of Spatial Parametric Methods via Regularization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, pp. 11335-11351. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2021.3120281. ISSN 1939-1404.

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Official URL: https://ieeexplore.ieee.org/document/9573368?source=authoralert

Abstract

Abstract—The spatial spectral estimation problem has applications in a variety of fields, including radar, telecommunications, and biomedical engineering. Among the different ap-proaches for estimating the spatial spectral pattern, there are several parametric methods, as the well-known multiple signal classification (MUSIC). Parametric methods like MUSIC are reduced to the problem of selecting an integer-valued parameter [so-called model order (MO)], which describes the number of signals impinging on the sensors array. Commonly, the best MO corresponds to the actual number of targets, nonetheless, relatively large model orders also retrieve good-fitted responses when the data generating mechanism is more complex than the models used to fit it. Most commonly employed MO selection (MOS) tools are based on information theoretic criteria [e.g., Akaike information criterion (AIC), minimum description length (MDL) and efficient detection criterion (EDC)]. Normally, the implementation of these tools involves the eigenvalues decomposition of the data covariance matrix. A major drawback of such parametric methods (together with certain MOS tool) is the drastic accuracy decrease in adverse scenarios, particularly, with low signal-to-noise ratio, since the separation of the signal and noise sub-spaces becomes more difficult to achieve. Conse-quently, with the aim of refining the responses attained by par-ametric techniques like MUSIC, this article suggests utilizing regularization as a post-processing step. Furthermore, as an alternative, this work also explores the possibility of selecting a single relatively large MO (rather than using MOS tools) and enhancing via regularization, the solutions retrieved by the treated parametric methods. In order to demonstrate the capabilities of this novel strategy, synthetic aperture radar (SAR) tomography (TomoSAR) is considered as application.

Item URL in elib:https://elib.dlr.de/146298/
Document Type:Article
Title:Resolution Enhancement of Spatial Parametric Methods via Regularization
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Martin del Campo Becerra, GustavoGustavo.MartindelCampoBecerra (at) dlr.dehttps://orcid.org/0000-0003-1642-6068
Serafín García, Sergio AlejandroSergio.SerafinGarcia (at) dlr.dehttps://orcid.org/0000-0003-2986-3793
Reigber, AndreasAndreas.Reigber (at) dlr.dehttps://orcid.org/0000-0002-2118-5046
Ortega Cisneros, Susanasortega (at) gdl.cinvestav.mxUNSPECIFIED
Nannini, MatteoMatteo.Nannini (at) dlr.deUNSPECIFIED
Date:November 2021
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:14
DOI :10.1109/JSTARS.2021.3120281
Page Range:pp. 11335-11351
Publisher:IEEE - Institute of Electrical and Electronics Engineers
Series Name:IGARSS 2021
ISSN:1939-1404
Status:Published
Keywords:Index Terms—Information criteria, maximum likelihood (ML), model order selection (MOS), synthetic aperture radar (SAR) tomography (TomoSAR), regularization.
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Aircraft SAR
Location: Oberpfaffenhofen
Institutes and Institutions:Microwaves and Radar Institute > SAR Technology
Deposited By: Martin del Campo Becerra, Gustavo
Deposited On:29 Nov 2021 08:26
Last Modified:24 May 2022 23:47

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