Robust layered sensing: From sparse signals to sparse residuals

Vasileios Kekatos, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

One of the key challenges in sensing networks is the extraction of information by fusing data from a multitude of possibly unreliable sensors. Robust sensing, viewed here as the simultaneous recovery of the wanted information-bearing signal vector together with the subset of (un)reliable sensors, is a problem whose optimum solution incurs combinatorial complexity. The present paper relaxes this problem to its closest convex approximation that turns out to yield a vector-generalization of Huber's scalar criterion for robust linear regression. The novel generalization is shown equivalent to a second-order cone program (SOCP), and exploits the block-sparsity inherent to a suitable model of the residuals. A computationally efficient solver is developed using a block-coordinate descent algorithm, and is tested with simulations.

Original languageEnglish (US)
Title of host publicationConference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Pages803-807
Number of pages5
DOIs
StatePublished - Dec 1 2010
Event44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States
Duration: Nov 7 2010Nov 10 2010

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
CountryUnited States
CityPacific Grove, CA
Period11/7/1011/10/10

Fingerprint Dive into the research topics of 'Robust layered sensing: From sparse signals to sparse residuals'. Together they form a unique fingerprint.

Cite this