Paper Info
Title
Nonparametric Simultaneous Sparse Recovery: an Application to Source Localization.
Abstract
We consider multichannel sparse recovery problem where the objective is to find good recovery of jointly sparse unknown signal vectors from the given multiple measurement vectors which are different linear combinations of the same known elementary vectors. Many popular greedy or convex algorithms perform poorly under non-Gaussian heavy-tailed noise conditions or in the face of outliers. In this paper, we propose the usage of mixed l(p,q) norms on data fidelity (residual matrix) term and die conventional (l(0,2)-norm constraint on the signal matrix to promote row-sparsity. We devise a greedy pursuit algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Simulation studies highlight the effectiveness of the proposed approaches to cope with different noise environments (i.i.d., row i.i.d, etc) and outliers. Usefulness of the methods are illustrated in source localization application with sensor arrays.
Year
Venue
Keywords
2015
European Signal Processing Conference
multichannel sparse recovery,compressed sensing,robustness,iterative hard thresholding
Field
DocType
Volume
Residual,Linear combination,Pattern recognition,Computer science,Matrix (mathematics),Sparse approximation,Signal-to-noise ratio,Robustness (computer science),Artificial intelligence,Thresholding,Sparse matrix
Journal
abs/1502.02441
ISSN
Citations 
PageRank 
2076-1465
2
0.39
References 
Authors
10
1
Name
Order
Citations
PageRank
Esa Ollila135133.51