3-D target feature extraction via interferometric SAR

The authors consider 3-D (three-dimensional) target feature extraction via an interferometric synthetic aperture radar (IFSAR). The targets of interest are relatively small and consist of a small number of distinct point scatterers. Since using IFSAR to extract the features of such targets has not been addressed before, a self-contained detailed derivation of the data model is presented. A set of sufficient parameter identifiability conditions on the data model and the Cramer-Rao bounds (CRBs) on the parameter estimates are also derived. Four existing two-dimensional feature extraction methods (FFT, windowed FFT, Capon and MUSIC) are extended to estimate the 3-D parameters of the target scatterers. A new nonlinear least squares parameter estimation method, referred to as IFRELAX, is also derived to extract the target features. Finally, numerical examples are presented to compare the performances of the presented methods with each other and with the corresponding CRBs. The authors show by means of numerical examples that, among the three nonparametric methods (FFT, windowed FFT, and Capon), Capon has the best resolution. The parametric methods MUSIC and IFRELAX can have much better resolution and provide much more accurate parameter estimates than the nonparametric methods. It is shown IFRELAX can be faster and provide much better parameter estimates than MUSIC