Sparse sensing for composite matched subspace detection

Mario Coutino; Sundeep Prabhakar Chepuri; Geert Leus

doi:10.1109/CAMSAP.2017.8313125

Sparse sensing for composite matched subspace detection

Mario Coutino, Sundeep Prabhakar Chepuri, Geert Leus

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

22 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 language	English
Title of host publication	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Place of Publication	Piscataway, NJ
Publisher	IEEE
Pages	1-5
Number of pages	5
ISBN (Electronic)	978-1-5386-1251-4
ISBN (Print)	978-1-5386-1252-1
DOIs	https://doi.org/10.1109/CAMSAP.2017.8313125
Publication status	Published - 2018
Event	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing - Willemstad, Curaçao Duration: 10 Dec 2017 → 13 Dec 2017 Conference number: 7 http://www.cs.huji.ac.il/conferences/CAMSAP17/

Workshop

Workshop	2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing
Abbreviated title	CAMSAP
Country/Territory	Curaçao
City	Willemstad
Period	10/12/17 → 13/12/17
Internet address	http://www.cs.huji.ac.il/conferences/CAMSAP17/

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

Access to Document

10.1109/CAMSAP.2017.8313125

08313125Final published version, 157 KB

Cite this

@inproceedings{7a1a90dd7b1f406e9882637eef91ff29,

title = "Sparse sensing for composite matched subspace detection",

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.",

keywords = "composite hypothesis testing, convex optimization, matched subspace detector, sensor selection, submodular optimization",

author = "Mario Coutino and Chepuri, {Sundeep Prabhakar} and Geert Leus",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – 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. ; 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP ; Conference date: 10-12-2017 Through 13-12-2017",

year = "2018",

doi = "10.1109/CAMSAP.2017.8313125",

language = "English",

isbn = "978-1-5386-1252-1",

pages = "1--5",

booktitle = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)",

publisher = "IEEE",

address = "United States",

url = "http://www.cs.huji.ac.il/conferences/CAMSAP17/",

}

Coutino, M, Chepuri, SP & Leus, G 2018, Sparse sensing for composite matched subspace detection. in 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). IEEE, Piscataway, NJ, pp. 1-5, 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, Willemstad, Curaçao, 10/12/17. https://doi.org/10.1109/CAMSAP.2017.8313125

Sparse sensing for composite matched subspace detection. / Coutino, Mario; Chepuri, Sundeep Prabhakar; Leus, Geert.
2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). Piscataway, NJ: IEEE, 2018. p. 1-5.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Sparse sensing for composite matched subspace detection

AU - Coutino, Mario

AU - Chepuri, Sundeep Prabhakar

AU - Leus, Geert

N1 - Conference code: 7

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - composite hypothesis testing

KW - convex optimization

KW - matched subspace detector

KW - sensor selection

KW - submodular optimization

UR - http://www.scopus.com/inward/record.url?scp=85050775575&partnerID=8YFLogxK

U2 - 10.1109/CAMSAP.2017.8313125

DO - 10.1109/CAMSAP.2017.8313125

M3 - Conference contribution

AN - SCOPUS:85050775575

SN - 978-1-5386-1252-1

SP - 1

EP - 5

BT - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

PB - IEEE

CY - Piscataway, NJ

T2 - 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing

Y2 - 10 December 2017 through 13 December 2017

ER -

Sparse sensing for composite matched subspace detection

Abstract

Workshop

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this