Paper Info
Title
Multichannel sparse recovery of complex-valued signals using Huber's criterion
Abstract
In this paper, we generalize Huber's criterion to multichannel sparse recovery problem of complex-valued measurements 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. This requires careful characterization of robust complex-valued loss functions as well as Huber's criterion function for the multivariate sparse regression problem. We devise a greedy algorithm based on simultaneous normalized iterative hard thresholding (SNIHT) algorithm. Unlike the conventional SNIHT method, our algorithm, referred to as HUB-SNIHT, is robust under heavy-tailed non-Gaussian noise conditions, yet has a negligible performance loss compared to SNIHT under Gaussian noise. Usefulness of the method is illustrated in source localization application with sensor arrays.
Year
DOI
Venue
2015
10.1109/CoSeRa.2015.7330257
2015 3rd International Workshop on Compressed Sensing Theory and its Applications to Radar, Sonar and Remote Sensing (CoSeRa)
Keywords
Field
DocType
complex-valued signal multichannel sparse recovery,Huber criterion,multiple measurement vector,elementary vector linear combination,multivariate sparse regression problem,greedy algorithm,simultaneous normalized iterative hard thresholding algorithm,SNIHT algorithm,heavy-tailed nonGaussian noise condition,Gaussian noise,sensor array,source localization application
Linear combination,Mathematical optimization,Normalization (statistics),Multivariate statistics,Computer science,Sparse approximation,Greedy algorithm,Thresholding,Criterion function,Gaussian noise
Journal
Volume
Citations 
PageRank 
abs/1504.04184
10
0.60
References 
Authors
13
1
Name
Order
Citations
PageRank
Esa Ollila135133.51