Resolution Enhancement of Spatial Parametric Methods via Regularization
The spatial spectral estimation problem has applications in a variety of fields, including radar, telecommunications, and biomedical engineering. Among the different approaches for estimating the spatial spectral pattern, there are several parametric methods, as the well-known multiple signal classi...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/9573368/ |
| _version_ | 1819002694016172032 |
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| author | Gustavo Martin-del-Campo-Becerra Sergio Serafin-Garcia Andreas Reigber Susana Ortega-Cisneros Matteo Nannini |
| author_facet | Gustavo Martin-del-Campo-Becerra Sergio Serafin-Garcia Andreas Reigber Susana Ortega-Cisneros Matteo Nannini |
| author_sort | Gustavo Martin-del-Campo-Becerra |
| collection | DOAJ |
| description | The spatial spectral estimation problem has applications in a variety of fields, including radar, telecommunications, and biomedical engineering. Among the different approaches 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, minimum description length and efficient detection criterion). 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 subspaces becomes more difficult to achieve. Consequently, with the aim of refining the responses attained by parametric techniques like MUSIC, this article suggests utilizing regularization as a postprocessing step. Furthermore, as an alternative, this article 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 tomography is considered as application. |
| first_indexed | 2024-12-20T23:09:10Z |
| format | Article |
| id | doaj.art-6aec7b32628646d0b9de47a51947251d |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-12-20T23:09:10Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-6aec7b32628646d0b9de47a51947251d2022-12-21T19:23:47ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352021-01-0114113351135110.1109/JSTARS.2021.31202819573368Resolution Enhancement of Spatial Parametric Methods via RegularizationGustavo Martin-del-Campo-Becerra0https://orcid.org/0000-0003-1642-6068Sergio Serafin-Garcia1https://orcid.org/0000-0003-2986-3793Andreas Reigber2https://orcid.org/0000-0002-2118-5046Susana Ortega-Cisneros3https://orcid.org/0000-0001-6646-1529Matteo Nannini4https://orcid.org/0000-0003-3523-9639Microwaves and Radar Institute, German Aerospace Center, Oberpfaffenhofen, GermanyCenter for Research and Advanced Studies (Cinvestav), National Polytechnic Institute (IPN), Zapopan, Jalisco, MexicoMicrowaves and Radar Institute, German Aerospace Center, Oberpfaffenhofen, GermanyCenter for Research and Advanced Studies (Cinvestav), National Polytechnic Institute (IPN), Zapopan, Jalisco, MexicoMicrowaves and Radar Institute, German Aerospace Center, Oberpfaffenhofen, GermanyThe spatial spectral estimation problem has applications in a variety of fields, including radar, telecommunications, and biomedical engineering. Among the different approaches 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, minimum description length and efficient detection criterion). 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 subspaces becomes more difficult to achieve. Consequently, with the aim of refining the responses attained by parametric techniques like MUSIC, this article suggests utilizing regularization as a postprocessing step. Furthermore, as an alternative, this article 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 tomography is considered as application.https://ieeexplore.ieee.org/document/9573368/Information criteriamaximum likelihood (ML)model order selection (MOS)regularizationsynthetic aperture radar (SAR) tomography (TomoSAR) |
| spellingShingle | Gustavo Martin-del-Campo-Becerra Sergio Serafin-Garcia Andreas Reigber Susana Ortega-Cisneros Matteo Nannini Resolution Enhancement of Spatial Parametric Methods via Regularization IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Information criteria maximum likelihood (ML) model order selection (MOS) regularization synthetic aperture radar (SAR) tomography (TomoSAR) |
| title | Resolution Enhancement of Spatial Parametric Methods via Regularization |
| title_full | Resolution Enhancement of Spatial Parametric Methods via Regularization |
| title_fullStr | Resolution Enhancement of Spatial Parametric Methods via Regularization |
| title_full_unstemmed | Resolution Enhancement of Spatial Parametric Methods via Regularization |
| title_short | Resolution Enhancement of Spatial Parametric Methods via Regularization |
| title_sort | resolution enhancement of spatial parametric methods via regularization |
| topic | Information criteria maximum likelihood (ML) model order selection (MOS) regularization synthetic aperture radar (SAR) tomography (TomoSAR) |
| url | https://ieeexplore.ieee.org/document/9573368/ |
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