Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks

Vipul Gupta; Bhavya Kailkhura; Thakshila Wimalajeewa; Sijia Liu; Pramod Kumar Varshney

doi:10.1109/ACSSC.2015.7421389

Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks

Vipul Gupta, Bhavya Kailkhura, Thakshila Wimalajeewa, Sijia Liu, Pramod Kumar Varshney

Department of Electrical Engineering & Computer Science

Research output: Chapter in Book/Entry/Poem › Conference contribution

5 Scopus citations

Abstract

We study the problem of joint sparsity pattern recovery with 1-bit compressive measurements in a sensor network. Sensors are assumed to observe sparse signals having the same but unknown sparsity pattern. Each sensor quantizes its measurement vector element-wise to 1-bit and transmits the quantized observations to a fusion center. We develop a computationally tractable support recovery algorithm which minimizes a cost function defined in terms of the likelihood function and the ℓ1,∞ norm. We observe that even with noisy 1-bit measurements, joint sparsity pattern can be recovered accurately with multiple sensors each collecting only a small number of measurements.

Original language	English (US)
Title of host publication	Conference Record - Asilomar Conference on Signals, Systems and Computers
Publisher	IEEE Computer Society
Pages	1472-1476
Number of pages	5
Volume	2016-February
ISBN (Print)	9781467385763
DOIs	https://doi.org/10.1109/ACSSC.2015.7421389
State	Published - Feb 26 2016
Event	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015 - Pacific Grove, United States Duration: Nov 8 2015 → Nov 11 2015

Other

Other	49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015
Country/Territory	United States
City	Pacific Grove
Period	11/8/15 → 11/11/15

Keywords

Compressed sensing
maximum-likelihood estimation
quantization
support recovery

ASJC Scopus subject areas

Computer Networks and Communications
Signal Processing

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10.1109/ACSSC.2015.7421389

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Gupta, V, Kailkhura, B, Wimalajeewa, T, Liu, S & Varshney, PK 2016, Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks. in Conference Record - Asilomar Conference on Signals, Systems and Computers. vol. 2016-February, 7421389, IEEE Computer Society, pp. 1472-1476, 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015, Pacific Grove, United States, 11/8/15. https://doi.org/10.1109/ACSSC.2015.7421389

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title = "Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks",

abstract = "We study the problem of joint sparsity pattern recovery with 1-bit compressive measurements in a sensor network. Sensors are assumed to observe sparse signals having the same but unknown sparsity pattern. Each sensor quantizes its measurement vector element-wise to 1-bit and transmits the quantized observations to a fusion center. We develop a computationally tractable support recovery algorithm which minimizes a cost function defined in terms of the likelihood function and the ℓ1,∞ norm. We observe that even with noisy 1-bit measurements, joint sparsity pattern can be recovered accurately with multiple sensors each collecting only a small number of measurements.",

keywords = "Compressed sensing, maximum-likelihood estimation, quantization, support recovery",

author = "Vipul Gupta and Bhavya Kailkhura and Thakshila Wimalajeewa and Sijia Liu and Varshney, {Pramod Kumar}",

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AU - Gupta, Vipul

AU - Kailkhura, Bhavya

AU - Wimalajeewa, Thakshila

AU - Liu, Sijia

AU - Varshney, Pramod Kumar

PY - 2016/2/26

Y1 - 2016/2/26

N2 - We study the problem of joint sparsity pattern recovery with 1-bit compressive measurements in a sensor network. Sensors are assumed to observe sparse signals having the same but unknown sparsity pattern. Each sensor quantizes its measurement vector element-wise to 1-bit and transmits the quantized observations to a fusion center. We develop a computationally tractable support recovery algorithm which minimizes a cost function defined in terms of the likelihood function and the ℓ1,∞ norm. We observe that even with noisy 1-bit measurements, joint sparsity pattern can be recovered accurately with multiple sensors each collecting only a small number of measurements.

AB - We study the problem of joint sparsity pattern recovery with 1-bit compressive measurements in a sensor network. Sensors are assumed to observe sparse signals having the same but unknown sparsity pattern. Each sensor quantizes its measurement vector element-wise to 1-bit and transmits the quantized observations to a fusion center. We develop a computationally tractable support recovery algorithm which minimizes a cost function defined in terms of the likelihood function and the ℓ1,∞ norm. We observe that even with noisy 1-bit measurements, joint sparsity pattern can be recovered accurately with multiple sensors each collecting only a small number of measurements.

KW - Compressed sensing

KW - maximum-likelihood estimation

KW - quantization

KW - support recovery

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VL - 2016-February

SP - 1472

EP - 1476

BT - Conference Record - Asilomar Conference on Signals, Systems and Computers

PB - IEEE Computer Society

T2 - 49th Asilomar Conference on Signals, Systems and Computers, ACSSC 2015

Y2 - 8 November 2015 through 11 November 2015

ER -

Joint sparsity pattern recovery with 1-bit compressive sensing in sensor networks

Abstract

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Keywords

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