ω − k MUSIC Algorithm for Subsurface Target Localization.
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| Title: | ω − k MUSIC Algorithm for Subsurface Target Localization. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs17162838 |
