Sparse Signal Methods for 3-D Radar Imaging

Synthetic aperture radar (SAR) imaging is a valuable tool in a number of defense surveillance and monitoring applications. There is increasing interest in 3-D reconstruction of objects from radar measurements. Traditional 3-D SAR image formation requires data collection over a densely sampled azimuth-elevation sector. In practice, such a dense measurement set is difficult or impossible to obtain, and effective 3-D reconstructions using sparse measurements are sought. This paper presents wide-angle 3-D image reconstruction approaches for object reconstruction that exploit reconstruction sparsity in the signal domain to ameliorate the limitations of sparse measurements. Two methods are presented; first, we use ℓ_p penalized (for p ≤ 1) least squares inversion, and second, we utilize tomographic SAR processing to derive wide-angle 3-D reconstruction algorithms that are computationally attractive but apply to a specific class of sparse aperture samplings. All approaches rely on high-frequency radar backscatter phenomenology so that sparse signal representations align with physical radar scattering properties of the objects of interest. We present full 360° 3-D SAR visualizations of objects from air-to-ground X-band radar measurements using different flight paths to illustrate and compare the two approaches.