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

School of Engineering

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

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-infinity reweighted L-1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.

Original language	Undefined/Unknown
Title of host publication	2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages	3311-3315
Number of pages	5
DOIs	https://doi.org/10.1109/ICASSP.2017.7952769
Publication status	Published - 1 Mar 2017

Keywords / Materials (for Non-textual outputs)

estimation theory
minimisation
wireless sensor networks
L-1/L-infinity norm minimization
balanced sensor management
binary optimization problem
convex relaxation
multiple time instances
regularized L-infinity reweighted L-1 norm minimization
sensor network estimation
Electronic mail
Estimation
Linear programming
Measurement uncertainty
Minimization
Optimization
Volume measurement
binary optimization
convex optimization
sensor management

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10.1109/ICASSP.2017.7952769

Signal Processing in the Networked Battlespace
Mulgrew, B., Davies, M., Hopgood, J. & Thompson, J.
EPSRC
1/04/13 → 30/06/18
Project: Research

Cite this

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title = "Balanced sensor management across multiple time instances via l-1/l-infinity norm minimization",

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-infinity reweighted L-1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.",

keywords = "estimation theory, minimisation, wireless sensor networks, L-1/L-infinity norm minimization, balanced sensor management, binary optimization problem, convex relaxation, multiple time instances, regularized L-infinity reweighted L-1 norm minimization, sensor network estimation, Electronic mail, Estimation, Linear programming, Measurement uncertainty, Minimization, Optimization, Volume measurement, binary optimization, convex optimization, sensor management",

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

year = "2017",

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doi = "10.1109/ICASSP.2017.7952769",

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T1 - Balanced sensor management across multiple time instances via l-1/l-infinity norm minimization

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AU - Thompson, John

AU - Robertson, Neil M.

PY - 2017/3/1

Y1 - 2017/3/1

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-infinity reweighted L-1 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-infinity reweighted L-1 norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.

KW - estimation theory

KW - minimisation

KW - wireless sensor networks

KW - L-1/L-infinity norm minimization

KW - balanced sensor management

KW - binary optimization problem

KW - convex relaxation

KW - multiple time instances

KW - regularized L-infinity reweighted L-1 norm minimization

KW - sensor network estimation

KW - Electronic mail

KW - Estimation

KW - Linear programming

KW - Measurement uncertainty

KW - Minimization

KW - Optimization

KW - Volume measurement

KW - binary optimization

KW - convex optimization

KW - sensor management

U2 - 10.1109/ICASSP.2017.7952769

DO - 10.1109/ICASSP.2017.7952769

M3 - Conference contribution

SP - 3311

EP - 3315

BT - 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

ER -

Balanced sensor management across multiple time instances via l-1/l-infinity norm minimization

Abstract

Keywords / Materials (for Non-textual outputs)

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Projects

Signal Processing in the Networked Battlespace

Cite this