Performance Analysis of Stochastic Signal Detection with Compressive Measurements

Thakshila Wimalajeewa; Hao Chen; Pramod K. Varshney

doi:10.1109/ACSSC.2010.5757678

Performance Analysis of Stochastic Signal Detection with Compressive Measurements

Thakshila Wimalajeewa, Hao Chen, Pramod K. Varshney

Electrical Engineering Department

Syracuse University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Scopus citations

Abstract

Compressed sensing (CS) enables the recovery of sparse or compressible signals from relatively a small number of randomized measurements compared to Nyquist-rate samples. Although most of the CS literature has focused on sparse signal recovery, exact recovery is not actually necessary in many signal processing applications. Solving inference problems with compressive measurements has been addressed by recent CS literature. This paper takes some further steps to investigate the potential of CS in signal detection problems. We provide theoretical performance limits verified by simulations for detection performance in arbitrary random signal detection with compressive measurements.

Original language	American English
Title of host publication	2010 44th Asilomar Conference on Signals, Systems and Computers
Subtitle of host publication	November 7-10 2010, Piscataway, NJ
Pages	813-817
Number of pages	5
DOIs	https://doi.org/10.1109/ACSSC.2010.5757678
State	Published - 2010
Event	44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States Duration: 7 Nov 2010 → 10 Nov 2010

Publication series

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

Conference

Conference	44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Country/Territory	United States
City	Pacific Grove, CA
Period	7/11/10 → 10/11/10

EGS Disciplines

Electrical and Computer Engineering

Access to Document

10.1109/ACSSC.2010.5757678

Cite this

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title = "Performance Analysis of Stochastic Signal Detection with Compressive Measurements",

abstract = "Compressed sensing (CS) enables the recovery of sparse or compressible signals from relatively a small number of randomized measurements compared to Nyquist-rate samples. Although most of the CS literature has focused on sparse signal recovery, exact recovery is not actually necessary in many signal processing applications. Solving inference problems with compressive measurements has been addressed by recent CS literature. This paper takes some further steps to investigate the potential of CS in signal detection problems. We provide theoretical performance limits verified by simulations for detection performance in arbitrary random signal detection with compressive measurements.",

author = "Thakshila Wimalajeewa and Hao Chen and Varshney, {Pramod K.}",

year = "2010",

doi = "10.1109/ACSSC.2010.5757678",

language = "American English",

isbn = "9781424497218",

series = "Asilomar Conference on Signals, Systems and Computers",

pages = "813--817",

booktitle = "2010 44th Asilomar Conference on Signals, Systems and Computers",

note = "44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 ; Conference date: 07-11-2010 Through 10-11-2010",

}

Wimalajeewa, T, Chen, H & Varshney, PK 2010, Performance Analysis of Stochastic Signal Detection with Compressive Measurements. in 2010 44th Asilomar Conference on Signals, Systems and Computers: November 7-10 2010, Piscataway, NJ., 5757678, Asilomar Conference on Signals, Systems and Computers, pp. 813-817, 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010, Pacific Grove, CA, United States, 7/11/10. https://doi.org/10.1109/ACSSC.2010.5757678

Performance Analysis of Stochastic Signal Detection with Compressive Measurements. / Wimalajeewa, Thakshila; Chen, Hao; Varshney, Pramod K.
2010 44th Asilomar Conference on Signals, Systems and Computers: November 7-10 2010, Piscataway, NJ. 2010. p. 813-817 5757678 (Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wimalajeewa, Thakshila

AU - Chen, Hao

AU - Varshney, Pramod K.

PY - 2010

Y1 - 2010

N2 - Compressed sensing (CS) enables the recovery of sparse or compressible signals from relatively a small number of randomized measurements compared to Nyquist-rate samples. Although most of the CS literature has focused on sparse signal recovery, exact recovery is not actually necessary in many signal processing applications. Solving inference problems with compressive measurements has been addressed by recent CS literature. This paper takes some further steps to investigate the potential of CS in signal detection problems. We provide theoretical performance limits verified by simulations for detection performance in arbitrary random signal detection with compressive measurements.

AB - Compressed sensing (CS) enables the recovery of sparse or compressible signals from relatively a small number of randomized measurements compared to Nyquist-rate samples. Although most of the CS literature has focused on sparse signal recovery, exact recovery is not actually necessary in many signal processing applications. Solving inference problems with compressive measurements has been addressed by recent CS literature. This paper takes some further steps to investigate the potential of CS in signal detection problems. We provide theoretical performance limits verified by simulations for detection performance in arbitrary random signal detection with compressive measurements.

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UR - https://scholarworks.boisestate.edu/electrical_facpubs/143

U2 - 10.1109/ACSSC.2010.5757678

DO - 10.1109/ACSSC.2010.5757678

M3 - Conference contribution

SN - 9781424497218

T3 - Asilomar Conference on Signals, Systems and Computers

SP - 813

EP - 817

BT - 2010 44th Asilomar Conference on Signals, Systems and Computers

T2 - 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010

Y2 - 7 November 2010 through 10 November 2010

ER -

Performance Analysis of Stochastic Signal Detection with Compressive Measurements

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