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An Autoregressive Formulation for SAR Backprojection Imaging

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·N2, where M is the recursion order (typically ≤ 3) and N2 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.