Bistatic SAR Point Spread Function Analysis for Close Proximity Geometries

Abstract

The goal of this project is to describe the spatially variant imaging performance of bistatic SAR imaging using a localized spatially invariant approximation so as to use spatial Fourier analysis. Bistatic synthetic aperture radar (SAR) has been proposed for use in search and rescue operations, for UAV guidance, and as an all-weather navigational aid for landing aircraft. These applications involve aggressive maneuvering platforms operating at short standoff ranges. Matched filter image formation (MFIF) and the equivalent back-projection image formation algorithm fully capture the intrinsic imaging performance of these geometries. The imaging performance for these imaging configurations is described by the spatially variant point spread function (PSF). The SAR imaging process is described as a non-convolutional integral operation between the scene’s reflectivity function and the PSF. It is desirable to describe the process as a multiplication operation in the spatial Fourier domain but this requires the imaging process to be spatially invariant. The model derived here deals with the spatial variations by segmenting a large scene into smaller regions. Within each region, the spatially variant PSF is locally approximated as a spatially invariant impulse response (IPR). The SAR imaging process is described as a localized convolution operation. This approximation is extremely accurate at the center of a segmented region but degrades for positions displaced from the center. Error analysis is used to determine the maximum size of a segment to limit the error to a given tolerance.