A
Ali Pezeshki
Researcher at Colorado State University
Publications - 145
Citations - 2827
Ali Pezeshki is an academic researcher from Colorado State University. The author has contributed to research in topics: Tree (data structure) & Compressed sensing. The author has an hindex of 20, co-authored 133 publications receiving 2507 citations. Previous affiliations of Ali Pezeshki include Princeton University & University of Melbourne.
Papers
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Journal ArticleDOI
Sensitivity to Basis Mismatch in Compressed Sensing
TL;DR: This paper establishes achievable bounds for the l1 error of the best k -term approximation and derives bounds, with similar growth behavior, for the basis pursuit l1 recovery error, indicating that the sparse recovery may suffer large errors in the presence of basis mismatch.
Proceedings ArticleDOI
Sensitivity to basis mismatch in compressed sensing
TL;DR: This paper establishes achievable bounds for the l1 error of the best k -term approximation and derives bounds, with similar growth behavior, for the basis pursuit l1 recovery error, indicating that the sparse recovery may suffer large errors in the presence of basis mismatch.
Journal ArticleDOI
Doppler Resilient Golay Complementary Waveforms
TL;DR: A method of constructing a sequence (pulse train) of phase-coded waveforms, for which the ambiguity function is free of range sidelobes along modest Doppler shifts, for radar polarimetry, where the two dimensions are realized by orthogonal polarizations.
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Robust dimension reduction, fusion frames, and Grassmannian packings
TL;DR: In this paper, the authors consider estimating a random vector from its measurements in a fusion frame, in the presence of noise and subspace erasures, and derive bounds on the mean-squared error (MSE) and show that the MSE will achieve its lower bound if the fusion frame is tight.
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Eigenvalue Beamforming Using a Multirank MVDR Beamformer and Subspace Selection
TL;DR: Eigenvalue beamformers are derived to resolve an unknown signal of interest whose spatial signature lies in a known subspace, but whose orientation in that subspace is otherwise unknown, and it is shown that the eigenvalues of an error covariance matrix are fundamental for resolving signals of interest.