Spectral compressive sensing

By Marco F. Duarte, Richard Baraniuk. 2013

426 citations recorded by the source corpus.

Subfields: Computational Mechanics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/7e34f62e18d2d0a71d9486659f3f8e828d4511df

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

1. Lihua Xie
2. Zai Yang
3. Yonina C. Eldar
4. Marco F. Duarte
5. Zhilin Zhang
6. B. Recht
7. Jian Li
8. P. Stoica

In Signal Processing

1. Lihua Xie
2. Zai Yang
3. Jian Li
4. P. Stoica
5. E.J. Candes
6. Marco F. Duarte
7. Yonina C. Eldar
8. Zhilin Zhang

Citations in the corpus

• Atomic Norm Denoising With Applications to Line Spectral Estimation (2012, methodology): “Compressive sensing based results (see, for instance, [48]) need to carefully control the incoherence of their linear maps to apply off-the-shelf tools from compressed sensing.”
• On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data (2014, positive): “Therefore, the atomic norm-based methods in [28] and later papers [31]–[33] correspond to gridless versions (or limiting scenarios with an infinitely dense grid) of the (cid:96) 1 -based meth-ods.”
• Sparse Methods for Direction-of-Arrival Estimation (2016, methodology): “To overcome the theoretical bottleneck mentioned above, the so-called coherence-inhibiting techniques have been proposed and incorporated in the existing sparse optimization methods to avoid solutions with closely located atoms [95, 96].”
• 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].”
• TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise (2014, background): “It is interesting to see that SSR can be used in spectrum estimation [19], [22].”
• Sensitivity to Basis Mismatch in Compressed Sensing (2010, neutral): “Another relevant study is presented in [48].”
• Exact Support Recovery for Sparse Spikes Deconvolution (2013, neutral): “This includes for instance greedy [26, 25], root finding [3, 11], matrix pencils [13] and compressed sensing [18, 16] approaches.”
• 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].”
• Towards a Mathematical Theory of Super‐resolution (2012, methodology): “Finally, we would also like to mention some very recent work on sparse recovery in highly coherent frames by modified greedy compressed sensing algorithms [17, 20].”
• Super-Resolution from Noisy Data (2012, neutral): “This observation has spurred the appearance of modified compressed-sensing techniques specifically tailored to the task of spectral estimation [10, 12, 19].”
• Tradeoffs Between Convergence Speed and Reconstruction Accuracy in Inverse Problems (2016, positive): “Section IV shows how it is possible to tradeoff between convergence speed and reconstruction accuracy by introducing the inexact projected gradient descent (IPGD) method using spectral compressed sensing [35] as a motivating example.”
• Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction (2015, methodology): “Using sparse signal recovery algorithms to estimate spectra has advantages over conventional nonparametric spectrum estimation methods and line spectral estimation methods, such as high spectrum resolution, low estimation variance, and increased robustness [18].”