Computational methods for sparse solution of linear inverse problems

JA Tropp, SJ Wright - Proceedings of the IEEE, 2010 - ieeexplore.ieee.org
The goal of the sparse approximation problem is to approximate a target signal using a
linear combination of a few elementary signals drawn from a fixed collection. This paper …

Signal recovery from random measurements via orthogonal matching pursuit

JA Tropp, AC Gilbert - IEEE Transactions on information theory, 2007 - ieeexplore.ieee.org
This paper demonstrates theoretically and empirically that a greedy algorithm called
Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in …

CoSaMP: Iterative signal recovery from incomplete and inaccurate samples

D Needell, JA Tropp - Applied and computational harmonic analysis, 2009 - Elsevier
Compressive sampling offers a new paradigm for acquiring signals that are compressible
with respect to an orthonormal basis. The major algorithmic challenge in compressive …

[图书][B] OFDM for underwater acoustic communications

S Zhou, Z Wang - 2014 - books.google.com
A blend of introductory material and advanced signal processing and communication
techniques, of critical importance to underwater system and network development This book …

CoSaMP: iterative signal recovery from incomplete and inaccurate samples

D Needell, JA Tropp - Communications of the ACM, 2010 - dl.acm.org
Compressive sampling (CoSa) is a new paradigm for developing data sampling
technologies. It is based on the principle that many types of vector-space data are …

Precise undersampling theorems

DL Donoho, J Tanner - Proceedings of the IEEE, 2010 - ieeexplore.ieee.org
Undersampling theorems state that we may gather far fewer samples than the usual
sampling theorem while exactly reconstructing the object of interest-provided the object in …

A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework

J Jin, Y Gu, S Mei - IEEE Journal of Selected Topics in Signal …, 2010 - ieeexplore.ieee.org
Based on the methodological similarity between sparse signal reconstruction and system
identification, a new approach for sparse signal reconstruction in compressive sensing (CS) …

Tree-structured compressive sensing with variational Bayesian analysis

L He, H Chen, L Carin - IEEE Signal Processing Letters, 2009 - ieeexplore.ieee.org
In compressive sensing (CS) the known structure in the transform coefficients may be
leveraged to improve reconstruction accuracy. We here develop a hierarchical statistical …

The group lasso for stable recovery of block-sparse signal representations

X Lv, G Bi, C Wan - IEEE Transactions on Signal Processing, 2011 - ieeexplore.ieee.org
Group Lasso is a mixed l 1/l 2-regularization method for a block-wise sparse model that has
attracted a lot of interests in statistics, machine learning, and data mining. This paper …

A* orthogonal matching pursuit: Best-first search for compressed sensing signal recovery

NB Karahanoglu, H Erdogan - Digital Signal Processing, 2012 - Elsevier
Compressed sensing is a developing field aiming at the reconstruction of sparse signals
acquired in reduced dimensions, which make the recovery process under-determined. The …