3D feature estimation for sparse, nonlinear bistatic SAR apertures

Abstract

We present an algorithm for extracting 3D canonical scattering features observed over sparse, bistatic SAR apertures. The input to the algorithm is a collection of noisy bistatic measurements which are, in general, collected over nonlinear flight paths. The output of the algorithm is a set of canonical scattering features that describe the 3D scene geometry. The algorithm employs a pragmatic approach to initializing feature estimates by first forming a 3D reflectivity reconstruction using sparsity-regularized least squares methods. Regions of high energy are detected in the reconstructions to obtain initial feature estimates. A single canonical feature, corresponding to a geometric shape primitive, is fit to each region via nonlinear optimization of fit error between the complex phase history data and parametric scattering models using a modification of the CLEAN method. Feature extraction results are presented for sparsely-sampled, nonlinear, 3D bistatic scattering prediction data of a simple scene.