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We introduce a dispersive point target model based on scattering by a particle in the far-field. The synthetic aperture imaging problem is then expanded to identify these targets and recover their locations and frequency dependent reflectivities. We show that Kirchhoff migration (KM) is able to identify dispersive point targets in an imaging region. However, KM predicts target locations that are shifted in range from their true locations. We derive an estimate for this range shift for a single target. We also show that because of this range shift we cannot recover the complex-valued frequency dependent reflectivity, but we can recover its absolute value and hence the radar cross-section (RCS) of the target. Simulation results show that we can detect, recover the approximate location, and recover the RCS for dispersive point targets thereby opening opportunities to classifying important differences between multiple targets such as their sizes or material compositions.
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This content will become publicly available on January 1, 2026
Spectral Classification of Targets Below a Random Rough Air–Soil Interface
Motivated by the use of unmanned aerial vehicles (UAVs) for buried landmine detection, we consider the spectral classification of dispersive point targets below a rough air-soil interface. The target location can be estimated using a previously developed method for ground-penetrating synthetic aperture radar involving principal component analysis for ground bounce removal and Kirchhoff migration. For the classification problem, we use the approximate location determined from this imaging method to recover the spectral characteristics of the target over the system bandwidth. For the dispersive point target we use here, this spectrum corresponds to its radar cross section (RCS). For a more general target, this recovered spectrum is a proxy for the frequency dependence of the RCS averaged over angles spanning the synthetic aperture. The recovered spectrum is noisy and exhibits an overall scaling error due to modeling errors. Nonetheless, by smoothing and normalizing this recovered spectrum, we compare it with a library of precomputed normalized spectra in a simple multiclass classification scheme. Numerical simulations in two dimensions validate this method and show that this spectral estimation method is effective for target classification.
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- Award ID(s):
- 1840265
- PAR ID:
- 10591635
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Geoscience and Remote Sensing Letters
- Volume:
- 22
- ISSN:
- 1545-598X
- Page Range / eLocation ID:
- 1 to 4
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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