Sparse sensing for composite matched subspace detection

Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Downloads (Pure)

Abstract

In this paper, we propose sensor selection strategies, based on convex and greedy approaches, for designing sparse samplers for composite detection. Particularly, we focus our attention on sparse samplers for matched subspace detectors. Differently from previous works, that mostly rely on random matrices to perform compression of the sub-spaces, we show how deterministic samplers can be designed under a Neyman-Pearson-like setting when the generalized likelihood ratio test is used. For a less stringent case than the worst case design, we introduce a submodular cost that obtains comparable results with its convex counterpart, while having a linear time heuristic for its near optimal maximization.

Original languageEnglish
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages1-5
Number of pages5
ISBN (Electronic)978-1-5386-1251-4
ISBN (Print)978-1-5386-1252-1
DOIs
Publication statusPublished - 2018
Event2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Willemstad, Curaçao
Duration: 10 Dec 201713 Dec 2017
Conference number: 7
http://www.cs.huji.ac.il/conferences/CAMSAP17/

Workshop

Workshop2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated titleCAMSAP
CountryCuraçao
CityWillemstad
Period10/12/1713/12/17
Internet address

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • composite hypothesis testing
  • convex optimization
  • matched subspace detector
  • sensor selection
  • submodular optimization

Fingerprint

Dive into the research topics of 'Sparse sensing for composite matched subspace detection'. Together they form a unique fingerprint.

Cite this