Abstract: A high definition radar imaging system receives SAR image data and adaptively processes the image the data to provide a high resolution SAR image. The imaging technique employs an adaptive filter whose tap weights are computed based upon a constrained Maximum Likelihood Method (MLM). The MLM technique chooses the filter tap weights (i.e., a weighting vector &ohgr;) to satisfy several criteria including: 1) they preserve unity gain for a point scatter at the desired location, and 2) they minimize the perceived energy in the output image. The weights &ohgr; are constrained in norm ∥&ohgr;∥≦&bgr;, to reduce the loss of sensitivity to bright scatters. Significantly, the present invention applies an additional constraint on the iterative selection of the weighting vector &ohgr;, such that the weighting vector &ohgr; shall confined to a particular subspace in order to preserve background information in the image.

Abstract: A high-definition radar imaging system and method receives image data and adaptively processes the image the data to provide a high resolution image. The imaging technique employs adaptive processing using a constrained minimum variance method to iteratively compute the high-definition image. The high-definition image I is expressed in range and cross-range as I(r,c)=min&ohgr;HR&ohgr;, where &ohgr; is a weighting vector and R is a covariance matrix of the image data. A solution for I(r,c) is approximated by i) forming Y=[x1 . . . xK]T/{square root over (K)} where x1 . . .

Abstract: A high-definition radar imaging system and method receives image data and adaptively processes the image the data to provide a high resolution image. The imaging technique employs adaptive processing using a constrained minimum variance method to iteratively compute the high-definition image. The high-definition image I is expressed in range and cross-range as I(r,c)=min&ohgr;HR&ohgr;, where &ohgr; is a weighting vector and R is a covariance matrix of the image data. A solution for I(r,c) is approximated by i) forming Y=[x1 . . . xK]T/{square root}{square root over (K)} where x1 . . .