Denoising by sparse approximation: error bounds based on rate-distortion theory
EURASIP Journal on Applied Signal Processing
A Distributed Spatio-temporal EEG/MEG Inverse Solver
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Algorithm 890: Sparco: A Testing Framework for Sparse Reconstruction
ACM Transactions on Mathematical Software (TOMS)
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Review of user parameter-free robust adaptive beamforming algorithms
Digital Signal Processing
Near-optimal Bayesian localization via incoherence and sparsity
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Sparse reconstruction by separable approximation
IEEE Transactions on Signal Processing
A compressive sensing data acquisition and imaging method for stepped frequency GPRs
IEEE Transactions on Signal Processing
A sparsity constrained inverse problem to locate people in a network of cameras
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Sparse Support Vector Machines with L_{p} Penalty for Biomarker Identification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust recovery of signals from a structured union of subspaces
IEEE Transactions on Information Theory
Direction finding of multiple emitters by spatial sparsity and linear programming
ISCIT'09 Proceedings of the 9th international conference on Communications and information technologies
Performance analysis of support recovery with joint sparsity constraints
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Bounds on the number of identifiable outliers in source localization by linear programming
IEEE Transactions on Signal Processing
Variance-component based sparse signal reconstruction and model selection
IEEE Transactions on Signal Processing
Theoretical and empirical results for recovery from multiple measurements
IEEE Transactions on Information Theory
A multi-sensor compressed sensing receiver: performance bounds and simulated results
Asilomar'09 Proceedings of the 43rd Asilomar conference on Signals, systems and computers
Source localization using a sparse representation framework to achieve superresolution
Multidimensional Systems and Signal Processing
Direction-of-arrival estimation using a mixed l2,0norm approximation
IEEE Transactions on Signal Processing
Performance analysis for sparse support recovery
IEEE Transactions on Information Theory
A multichannel spatial compressed sensing approach for direction of arrival estimation
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
SIAM Journal on Scientific Computing
Direct data domain STAP using sparse representation of clutter spectrum
Signal Processing
Sparsity Driven People Localization with a Heterogeneous Network of Cameras
Journal of Mathematical Imaging and Vision
Error bounds for convex parameter estimation
Signal Processing
Sparse representations and sphere decoding for array signal processing
Digital Signal Processing
Detection of sparse targets with structurally perturbed echo dictionaries
Digital Signal Processing
Bayesian compressive sensing as applied to directions-of-arrival estimation in planar arrays
Journal of Electrical and Computer Engineering - Special issue on Advances in Radar Technologies
Block-sparse recovery via redundant block OMP
Signal Processing
Generalized spike-and-slab priors for Bayesian group feature selection using expectation propagation
The Journal of Machine Learning Research
Covariance sparsity-aware DOA estimation for nonuniform noise
Digital Signal Processing
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We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the ℓ1-norm. A number of recent theoretical results on sparsifying properties of ℓ1 penalties justify this choice. Explicitly enforcing the sparsity of the representation is motivated by a desire to obtain a sharp estimate of the spatial spectrum that exhibits super-resolution. We propose to use the singular value decomposition (SVD) of the data matrix to summarize multiple time or frequency samples. Our formulation leads to an optimization problem, which we solve efficiently in a second-order cone (SOC) programming framework by an interior point implementation. We propose a grid refinement method to mitigate the effects of limiting estimates to a grid of spatial locations and introduce an automatic selection criterion for the regularization parameter involved in our approach. We demonstrate the effectiveness of the method on simulated data by plots of spatial spectra and by comparing the estimator variance to the Crame´r-Rao bound (CRB). We observe that our approach has a number of advantages over other source localization techniques, including increased resolution, improved robustness to noise, limitations in data quantity, and correlation of the sources, as well as not requiring an accurate initialization.