Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization

By Mujdat Cetin, W. C. Karl. IEEE Transactions on Image Processing, 2001

598 citations recorded by the source corpus.

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A Generalized Iterated Shrinkage Algorithm for Non-convex Sparse Coding (2013, background): “Actually, both theoretical analysis and numerical experiments [9, 10, 11] have shown that the solution of (cid:2) p -norm sparse coding (0 ≤ p 1) min is close to that of the (cid:2) 1 -minimization and it is sparser.”
Compressive Radar Imaging (2007, result): “The result is similar to what is obtained with the feature-enhanced imaging approach of [10].”
A sparse signal reconstruction perspective for source localization with sensor arrays (2005, background): “For example, [1, 2] is concerned with source localization in the beam-space domain, under the assumption that the sources are uncorrelated, and that a large number of time samples is available.”
Sparsity and Compressed Sensing in Radar Imaging (2010, methodology): “The algorithm in [31] employs an approximate Hessian and uses conjugate gradients”
Compressed Synthetic Aperture Radar (2010, methodology): “1932-4553/$26.00 © 2010 IEEE estimate of the reflectivity profile using this new image formation algorithm that relies on using a far fewer number of waveforms than the conventional systems do and requires no changes to a radar system hardware to work.”