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 proceedingChapter (peer-reviewed)

2 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 languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN (Electronic)978-1-5090-4117-6, ISSN:2379-190X
DOIs
Publication statusPublished - 19 Jun 2017
Event42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017) - New Orleans, United States
Duration: 05 Mar 201709 Mar 2017
http://www.ieee-icassp2017.org/

Publication series

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

Conference

Conference42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017)
CountryUnited States
CityNew Orleans
Period05/03/201709/03/2017
Internet address

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  • Cite this

    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