Balanced Sensor Management Across Multiple Time Instances via L-1/L-Infinity Norm Minimization

Cristian Rusu; John Thompson; Neil M. Robertson

doi:10.1109/ICASSP.2017.7952769

Balanced Sensor Management Across Multiple Time Instances via L-1/L-Infinity Norm Minimization

Cristian Rusu, John Thompson, Neil M. Robertson

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

3 Citations (Scopus)

Abstract

In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized ℓ∞ reweighted ℓ1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.

Original language	English
Title of host publication	2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN (Electronic)	978-1-5090-4117-6, ISSN:2379-190X
DOIs	https://doi.org/10.1109/ICASSP.2017.7952769
Publication status	Published - 19 Jun 2017
Event	IEEE International Conference on Acoustics, Speech, and Signal Processing 2017 - Hilton New Orleans Riverside, New Orleans, United States Duration: 05 Mar 2017 → 09 Mar 2017 http://www.ieee-icassp2017.org/ https://doi.org/10.1109/ICASSP31846.2017

Publication series

Name	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings
Publisher	IEEE
ISSN (Electronic)	2379-190X

Conference

Conference	IEEE International Conference on Acoustics, Speech, and Signal Processing 2017
Abbreviated title	ICASSP 2017
Country/Territory	United States
City	New Orleans
Period	05/03/2017 → 09/03/2017
Internet address	http://www.ieee-icassp2017.org/ https://doi.org/10.1109/ICASSP31846.2017

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10.1109/ICASSP.2017.7952769Licence: Unspecified

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Rusu, C., Thompson, J., & Robertson, N. M. (2017). Balanced Sensor Management Across Multiple Time Instances via L-1/L-Infinity Norm Minimization. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings). https://doi.org/10.1109/ICASSP.2017.7952769

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abstract = "In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized ℓ∞ reweighted ℓ1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.",

author = "Cristian Rusu and John Thompson and Robertson, {Neil M.}",

year = "2017",

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note = "IEEE International Conference on Acoustics, Speech, and Signal Processing 2017, ICASSP 2017 ; Conference date: 05-03-2017 Through 09-03-2017",

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Rusu, C, Thompson, J & Robertson, NM 2017, Balanced Sensor Management Across Multiple Time Instances via L-1/L-Infinity Norm Minimization. in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings, IEEE International Conference on Acoustics, Speech, and Signal Processing 2017, New Orleans, Louisiana, United States, 05/03/2017. https://doi.org/10.1109/ICASSP.2017.7952769

Balanced Sensor Management Across Multiple Time Instances via L-1/L-Infinity Norm Minimization. / Rusu, Cristian; Thompson, John; Robertson, Neil M.
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2017. (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed) › peer-review

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N2 - In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized ℓ∞ reweighted ℓ1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.

AB - In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized ℓ∞ reweighted ℓ1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.

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Y2 - 5 March 2017 through 9 March 2017

ER -

Balanced Sensor Management Across Multiple Time Instances via L-1/L-Infinity Norm Minimization

Abstract

Publication series

Conference

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