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This paper develops three-dimensional (3D), bistatic parametric models that describe canonical radar scattering responses of several geometric objects. These models find use in inverse scattering-based processing of high-frequency radar returns. Canonical feature models are useful for extracting geometry from synthetic-aperture radar (SAR) scattering measurements and as feature primitives for automatic target recognition (ATR) and scene visualization. Previous work has considered monostatic feature models for two-dimensional (2D) radar processing; we extend this work to consider bistatic and 3D radar apertures. In the work presented here, we generalize geometric theory of diffraction (GTD) solutions for several scattering mechanisms in a plane. Products of these planar mechanisms in azimuth and elevation are used to produce 3D bistatic scattering models for six canonical shapes: a rectangular plate, dihedral, trihedral, cylinder, top-hat, and sphere. The derived models are characterized by a small number of parameters, and are shown to agree with results obtained from high-frequency, asymptotic scattering simulations. [ABSTRACT FROM AUTHOR]

We propose a model for generating low-frequency synthetic aperture radar (SAR) clutter that relates model parameters to physical characteristics of the scene. The model includes both distributed scattering and large-amplitude discrete clutter responses. The model also incorporates the SAR imaging process, which introduces correlation among image pixels. The model may be used to generate synthetic clutter for a range of environmental operating conditions for use in target detection performance evaluation of the radar and automatic target detection/recognition algorithms. We derive a statistical representation of the proposed clutter model's pixel amplitudes and compare with measured data from the CARABAS.II SAR. Simulated clutter images capture the structure and amplitude responses seen in the measured data. A statistical analysis shows an order of magnitude improvement in model fit error compared with standard maximum-likelihood (ML) density fitting methods. [ABSTRACT FROM AUTHOR]

The polar format algorithm (PFA) for spotlight synthetic aperture radar (SAR) is based on a linear approximation for the differential range to a scatterer. We derive a second-order Taylor series approximation of the differential range. We provide a simple and concise derivation of both the far-field linear approximation of the differential range, which forms the basis of the PFA, and the corresponding approximation limits based on the second-order terms of the approximation. [ABSTRACT FROM AUTHOR]

Matched filtering (MIF) of phase history data is a mathematically ideal but computationally expensive approach to bistatic synthetic aperture radar (SAR) image formation. Fast backprojection algorithms (BPAs) for image formation have recently been shown to give improved O(N2 log2 N) performance. In this paper, an O(N2 log2 N) bistatic polar format algorithm (PFA) based on a bistatic far-field assumption is derived. This algorithm is a generalization of the popular PFA for monostatic SAR image formation and is highly amenable to implementation with existing monostatic image formation processors. Limits on the size of an imaged scene, analogous to those in monostatic systems, are derived for the bistatic PFA. [ABSTRACT FROM AUTHOR]

We present a recursive backprojection-based procedure for synthetic aperture imaging. In traditional synthetic aperture radar (SAR) imaging, radar measurements are processed in blocks of contiguous azimuth angle measurements. In continuous imaging applications, block processing generally entails significant computational overhead, in part due to overlapped processing of azimuth intervals. We consider a novel recursive update approach, in which the SAR image is updated using an autoregressive-based algorithm as new measurements are taken. In this approach, the SAR image is found as a linear combination of a small number of previous images and a term containing the current radar measurement. The computational and memory requirements of the recursive imaging approach are modest—on the order of M\cdot N^2, where M is the recursion order (typically \leq3) and N^2 is the image size. In addition, we investigate the crossrange sidelobes realized by the autoregressive (AR) imaging approach. We show that a first-order autoregression of the image gives crossrange sidelobes similar to a rectangular azimuth window, while a third-order autoregression gives sidelobes comparable to those obtained from widely-used windows in block-processing image formation. We compare images obtained from the recursive and block-processing techniques, both for a synthetic scene and for X-band SAR measurements from the Gotcha data set. [ABSTRACT FROM AUTHOR]