Ultrawideband SAR clutter suppression by AR spectral estimation

Scott W. Shaw

doi:10.1117/12.177186

9 June 1994 Ultrawideband SAR clutter suppression by AR spectral estimation

Scott W. Shaw

Proceedings Volume 2230, Algorithms for Synthetic Aperture Radar Imagery; (1994) https://doi.org/10.1117/12.177186
Event: SPIE's International Symposium on Optical Engineering and Photonics in Aerospace Sensing, 1994, Orlando, FL, United States

Abstract

Ultrawideband (UWB) SAR images often contain clutter and other types of spatially correlated noise. To reduce these effects, we have developed a complex, multichannel, autoregressive image- modeling and inverse-filtering algorithm. Our algorithm extends a previously reported technique to include complex image data and a solution to the directionality problem of 2D AR models. A multichannel image pixel, represented by a vector, is modeled as an autoregressive process with a nonsymmetric half-plane (NSHP) support region. The parameters of this NSHP AR model, represented as matrices, are estimated by minimizing a squared residual. To account for possible nonstationarity in the image statistics, the estimation is performed over a series of small subwindows, which lends a degree of adaptabtility to the algorithm. Once estimates of the complex AR parameter matrices are obtained, they are used in an inverse filter. To account for the directionality inherent in the NSHP filter, we have adopted a bi-directional filtering approach, with the results of the two filters combined such that targets are enhanced and clutter is minimized. This algorithm has been applied to various UWB SAR images and shown to be an effective whitening filter. The resulting signal-to-clutter enhancement depends on a variety of factors, including clutter statistics, target size, and AR model order. A version of the whitening algorithm has been incorporated into our standard set of tools for processing UWB SAR images.

Citation Download Citation

Scott W. Shaw "Ultrawideband SAR clutter suppression by AR spectral estimation", Proc. SPIE 2230, Algorithms for Synthetic Aperture Radar Imagery, (9 June 1994); https://doi.org/10.1117/12.177186

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KEYWORDS

Autoregressive models

Synthetic aperture radar

Image processing

Detection and tracking algorithms

Matrices

Data modeling

Algorithm development

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