Signal reconstruction in sensor arrays using sparse representations

Authors:
Dmitri Model;Michael Zibulevsky
Affiliations:
Technion--Israel Institute of Technology, Electrical Engineering Department, Haifa, Israel;Technion--Israel Institute of Technology, Electrical Engineering Department, Haifa, Israel
Venue:
Signal Processing - Sparse approximations in signal and image processing
Year:
2006

Citing 4
Cited 10

Atomic Decomposition by Basis Pursuit

SIAM Journal on Scientific Computing
A survey of truncated-Newton methods

Journal of Computational and Applied Mathematics - Special issue on numerical analysis 2000 Vol. IV: optimization and nonlinear equations
Array Signal Processing: Concepts and Techniques

Array Signal Processing: Concepts and Techniques
Blind Source Separation by Sparse Decomposition in a Signal Dictionary

Neural Computation

Constructing Time-Frequency Dictionaries for Source Separation via Time-Frequency Masking and Source Localisation

ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Near-optimal Bayesian localization via incoherence and sparsity

IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Point process models for spotting keywords in continuous speech

IEEE Transactions on Audio, Speech, and Language Processing
Performance analysis of support recovery with joint sparsity constraints

Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
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
Functional brain imaging with M/EEG using structured sparsity in time-frequency dictionaries

IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
Fast communication: A sparse recovery algorithm for DOA estimation using weighted subspace fitting

Signal Processing
Sound source localization using compressive sensing-based feature extraction and spatial sparsity

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

Quantified Score

Hi-index	0.06

Visualization

Abstract

We propose a technique of multisensor signal reconstruction based on the assumption, that source signals are spatially sparse, as well as have sparse representation in a chosen dictionary in time domain. This leads to a large scale convex optimization problem, which involves combined l1-l2 norm minimization. The optimization is carried by the truncated Newton method, using preconditioned conjugate gradients in inner iterations. The byproduct of reconstruction is the estimation of source locations.