Three Dimensional Imaging of Vehicles from Sparse Apertures in Urban Environment

Three-dimensional synthetic aperture radar (SAR) imaging of vehicles in urban setting are made possible by new data collection capabilities, in which airborne radar systems interrogate a large scene persistently and over a large range of aspect angles. Wide-angle 3-D reconstructions of vehicles can be useful in applications such as automatic target recognition (ATR) and fingerprinting. The backscatter data collected by the airborne platform at each pulse can be interpreted as 1-D lines of the 3-D Fourier transform of the scene, and the aggregation of radar returns over the flight path defines a conical manifold of data in the scenes 3-D Fourier domain. Generating high-resolution 3-D images using traditional Fourier processing methods requires that radar data be collected over a densely sampled set of points in both azimuth and elevation angle. This method of imaging requires very large collection times and storage requirements and may be prohibitively costly in practice. There is thus motivation to consider more sparsely sampled data collection strategies, where only a small fraction of the data required to perform traditional high-resolution imaging is collected. In this chapter, we review several techniques that have been proposed for 3-D reconstruction data collected from sparsely apertures, as well as discuss new approaches based on dictionary learning of target primitives. Particular emphasis is given sparsity regularized least squares approaches to wide-angle 3-D radar reconstruction for arbitrary, sparse apertures. We provide comprehensive set of comparative results using data from the GOTCHA data collection campaign. Chapter 11 3-D Imaging of Vehicles from Sparse Apertures 11.