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ω − k MUSIC Algorithm for Subsurface Target Localization.

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Title: ω − k MUSIC Algorithm for Subsurface Target Localization.
Authors: Cuccaro, Antonio1 (AUTHOR), Dell'Aversano, Angela2 (AUTHOR) angela.dellaversano@unicampania.it, Maisto, Maria Antonia2,3 (AUTHOR), Scapaticci, Rosa1,3 (AUTHOR), Brancaccio, Adriana2 (AUTHOR), Solimene, Raffaele2,3 (AUTHOR)
Source: Remote Sensing. Aug2025, Vol. 17 Issue 16, p2838. 16p.
Subjects: Multiple Signal Classification, Radar signal processing, Underground areas, Spatial resolution, Signal processing, Signal detection, Quantitative research
Abstract: This paper addresses the problem of subsurface target localization from single-snapshot multimonostatic and multifrequency radar measurements. In this context, the use of subspace projection methods—known for their super-resolution capabilities—is hindered by the rank deficiency of the data correlation matrix and the lack of a Vandermonde structure, especially in near-field configurations and layered media. To overcome this issue, we propose a novel pre-processing strategy that transforms the measured data into the ω − k domain, thereby restoring the structural conditions required for subspace-based detection. The resulting algorithm, referred to as ω − k MUSIC, enables the application of subspace projection techniques in scenarios where traditional smoothing procedures are not viable. Numerical experiments in a 2-D scalar configuration demonstrate the effectiveness of the proposed method in terms of resolution and robustness under various noise conditions. A Monte Carlo simulation study is also included to provide a quantitative assessment of localization accuracy. Comparisons with conventional migration imaging highlight the superior performance of the proposed approach. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: ω − k MUSIC Algorithm for Subsurface Target Localization.
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  Data: <searchLink fieldCode="AR" term="%22Cuccaro%2C+Antonio%22">Cuccaro, Antonio</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dell'Aversano%2C+Angela%22">Dell'Aversano, Angela</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> angela.dellaversano@unicampania.it</i><br /><searchLink fieldCode="AR" term="%22Maisto%2C+Maria+Antonia%22">Maisto, Maria Antonia</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Scapaticci%2C+Rosa%22">Scapaticci, Rosa</searchLink><relatesTo>1,3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Brancaccio%2C+Adriana%22">Brancaccio, Adriana</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Solimene%2C+Raffaele%22">Solimene, Raffaele</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Aug2025, Vol. 17 Issue 16, p2838. 16p.
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  Data: <searchLink fieldCode="DE" term="%22Multiple+Signal+Classification%22">Multiple Signal Classification</searchLink><br /><searchLink fieldCode="DE" term="%22Radar+signal+processing%22">Radar signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Underground+areas%22">Underground areas</searchLink><br /><searchLink fieldCode="DE" term="%22Spatial+resolution%22">Spatial resolution</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+processing%22">Signal processing</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink>
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  Data: This paper addresses the problem of subsurface target localization from single-snapshot multimonostatic and multifrequency radar measurements. In this context, the use of subspace projection methods—known for their super-resolution capabilities—is hindered by the rank deficiency of the data correlation matrix and the lack of a Vandermonde structure, especially in near-field configurations and layered media. To overcome this issue, we propose a novel pre-processing strategy that transforms the measured data into the ω − k domain, thereby restoring the structural conditions required for subspace-based detection. The resulting algorithm, referred to as ω − k MUSIC, enables the application of subspace projection techniques in scenarios where traditional smoothing procedures are not viable. Numerical experiments in a 2-D scalar configuration demonstrate the effectiveness of the proposed method in terms of resolution and robustness under various noise conditions. A Monte Carlo simulation study is also included to provide a quantitative assessment of localization accuracy. Comparisons with conventional migration imaging highlight the superior performance of the proposed approach. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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        Type: general
      – SubjectFull: Radar signal processing
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      – SubjectFull: Underground areas
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      – SubjectFull: Spatial resolution
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      – SubjectFull: Signal processing
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      – SubjectFull: Quantitative research
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      – TitleFull: ω − k MUSIC Algorithm for Subsurface Target Localization.
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              Text: Aug2025
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