Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Matrix computations (3rd ed.)
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Rethinking Biased Estimation: Improving Maximum Likelihood and the Cramér–Rao Bound
Foundations and Trends in Signal Processing
IEEE Transactions on Information Theory
On recovery of sparse signals via l1 minimization
IEEE Transactions on Information Theory
Robust recovery of signals from a structured union of subspaces
IEEE Transactions on Information Theory
Uncertainty relations for shift-invariant analog signals
IEEE Transactions on Information Theory
Block-sparse signals: uncertainty relations and efficient recovery
IEEE Transactions on Signal Processing
The Cramér-Rao bound for estimating a sparse parameter vector
IEEE Transactions on Signal Processing
Average case analysis of multichannel sparse recovery using convex relaxation
IEEE Transactions on Information Theory
Stable recovery of sparse signals and an oracle inequality
IEEE Transactions on Information Theory
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing
Uncertainty principles and ideal atomic decomposition
IEEE Transactions on Information Theory
Recovery of exact sparse representations in the presence of bounded noise
IEEE Transactions on Information Theory
Stable recovery of sparse overcomplete representations in the presence of noise
IEEE Transactions on Information Theory
Just relax: convex programming methods for identifying sparse signals in noise
IEEE Transactions on Information Theory
Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit
IEEE Transactions on Information Theory
The Cramér-Rao bound for estimating a sparse parameter vector
IEEE Transactions on Signal Processing
Estimating multiple frequency-hopping signal parameters via sparse linear regression
IEEE Transactions on Signal Processing
Regularized recursive least squares for anomaly detection in sparse channel tracking applications
Proceedings of the 2011 ACM Symposium on Research in Applied Computation
Journal of Approximation Theory
Detection of sparse targets with structurally perturbed echo dictionaries
Digital Signal Processing
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We consider the problem of estimating a deterministic sparse vector X0 from underdetermined measurements AX0 + w, where w represents white Gaussian noise and A is a given deterministic dictionary. We provide theoretical performance guarantees for three sparse estimation algorithms: basis pursuit denoising (BPDN), orthogonal matching pursuit (OMP), and thresholding. The performance of these techniques is quantified as the l2 distance between the estimate and the true value of X0. We demonstrate that, with high probability, the analyzed algorithms come close to the behavior of the oracle estimator, which knows the locations of the nonzero elements in X0. Our results are non-asymptotic and are based only on the coherence of A, so that they are applicable to arbitrary dictionaries. This provides insight on the advantages and drawbacks of l1 relaxation techniques such as BPDN and the Dantzig selector, as opposed to greedy approaches such as OMP and thresholding.