Sensitivity to Basis Mismatch in Compressed Sensing

By Yuejie Chi, Ali Pezeshki, Louis L. Scharf, Robert Calderbank. IEEE Transactions on Signal Processing, 2010

981 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/d26d070b81687d861e9e5a1b1a48b586debbef78

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In Biomedical Engineering

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Atomic Norm Denoising With Applications to Line Spectral Estimation (2012, result): “This observation does not contradict the results of [49] where the authors look at the full Fourier basis and the noise-free case.”
Super-Resolution from Noisy Data (2012, positive): “In addition, it is worth mentioning that working with a discrete dictionary can easily degrade the quality of the estimate [7] (see [40] for a related discussion concerning grid selection for spectral analysis), which highlights the importance of analyzing the problem in the continuous domain.”
Enabling Joint Communication and Radar Sensing in Mobile Networks—A Survey (2020, background): “exist a mismatch between the assumed and actual dictionaries, generally known as “dictionary mismatch”, which can cause significant performance degradation [191].”
Optimum Co-Design for Spectrum Sharing between Matrix Completion Based MIMO Radars and a MIMO Communication System (2015, methodology): “Unlike MIMO-CS, MIMO-MC does not require discretization of the target space, thus does not suffer from grid mismatch issues [27].”
Compressed Channel Estimation for Intelligent Reflecting Surface-Assisted Millimeter Wave Systems (2020, background): “In the presence of grid mismatch, the number of nonzero entries will become larger due to the power leakage caused by the grid mismatch [16].”
Sparsity-Cognizant Total Least-Squares for Perturbed Compressive Sampling (2010, methodology): “Bounds on the CS reconstruction error under basis mi match are provided in [12]; see also [10], where the mismatch-induced error was reduced by increasing the rid density.”
MIMO Radar for Advanced Driver-Assistance Systems and Autonomous Driving: Advantages and Challenges (2020, background): “Thus, the performance of compressive sensingbased methods is sensitive to targets appearing off the grid [79].”
Compressed Sensing Off the Grid (2012, background): “Digital Object Identi ﬁ er 10.1109/TIT.2013.2277451 signal cannot often be sparsely represented by the discrete dictionary [13], [17], [18].”
Deep Networks for Direction-of-Arrival Estimation in Low SNR (2020, background): “Off-grid and on-grid methods offer a more balanced solution with less computational demands at the expense of negligible loss in performance due to the grid mismatch problem [14].”
Sparse Methods for Direction-of-Arrival Estimation (2016, background): “Consequently, there is no guarantee that the true DOAs lie on the grid θ; in fact, this fails with probability one, resulting in the grid mismatch problem [79,80].”
Structured Compressed Sensing: From Theory to Applications (2011, background): “This causes a leakage of their energies in the quantized space into adjacent grid points [36], [87].”
Direction of Arrival Estimation Using Co-Prime Arrays: A Super Resolution Viewpoint (2013, background): “One merit of this theory is that it considers all the possible locations within the interested range, and thus does not suffer from model mismatch.”
Integrated Sensing and Communications: Recent Advances and Ten Open Challenges (2023, background): “Another family is constituted by the sparse signal reconstruction frameworks, including on-grid [103], off-grid [104], and gridless [105] algorithms.”