Environmental Research Institute of Michigan

Erim International, Inc.

Herbert C Stankwitz

Rodney J Dallaire

Michael R Kosek

Ken W Burgener

A high-order synthetic aperture radar (SAR) autofocusing method decomposes phase error into basis function components, one order at a time. Image patches are partitioned into a plurality of subapertures in accordance with positive- and negative-going slope of the basis function at each order. A positive mask is applied to each subaperture within which the basis function is increasing in slope, whereas a negative mask is applied to each subaperture within which the basis function is decreasing in slope. The results are then correlated to obtain a focused image, with the principle of map-drift preferably being used to compute phase-amplitude weights. The method may be uniformly applied, enabling the steps to be performed at a plurality of increasingly higher orders without performance degradation.

Subaperture high-order autofocus using reverse phase

In systems wherein signal compression is performed using matched filters or transforms, as in the case of radar, multiple extrapolations are used to resolve beyond the limits of defraction. In one example, development of the method begins with a complex, uniformly weighted SAR or inverse SAR signal represented by a rectangle function. After performing an FFT, adaptive sidelobe reduction is carried out followed by an inverse weighting and truncation, after which the original signal is used to replace the center portion of the extrapolated signal. The signal is again transformed and sidelobe reduced, and inverse weighting and truncation are again performed, followed by the original data replacement step. The extrapolation procedure may be repeated end times, extrapolating each time by a factor K for a total extrapolation factor of K=k.sup.n.

Super spatially variant apodization (Super - SVA)

Spatially variant apodization is a digital image processing technique for eliminating sidelobes produced by Fourier transform of finite data sequences without compromising mainlobe width. This process allows each sample or pixel in an image to receive its own frequency domain aperture amplitude weighting function from an infinite number of possible weighting functions. In its simplest form the weight is a function of the negative of the current sample divided by the sum of the neighboring samples, and the function is limited to a predetermined range such as the range between zero and one half.

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Ann Arbor, MI, United States of America

Herbert C Stankwitz