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.