Abstract

Finding spatial locations of physical sensors is critical to reliably estimating and monitoring spatiotemporal systems, such as weather, traffic, or social networks. Existing sensor placement approaches that leverage mutual information or coverage do not take into account the spatiotemporal dynamics of the underlying phenomena. Leveraging recent work in modeling evolving Gaussian processes, we show that a sensor placement method can be constructed by applying observability theory on linear models of the spatiotemporal phenomena in a higher dimensional feature space. We show that this approach outperforms traditional mutual information based approaches by taking into account the invariant subspaces induced by the spatiotemporal dynamics. Furthermore, fundamental results relating the observability of spatiotemporal phenomena with deterministic and stochastic sensors placement are proven.

Original languageEnglish (US)
Title of host publication2018 Annual American Control Conference, ACC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1058-1064
Number of pages7
ISBN (Print)9781538654286
DOIs
StatePublished - Aug 9 2018
Event2018 Annual American Control Conference, ACC 2018 - Milwauke, United States
Duration: Jun 27 2018Jun 29 2018

Publication series

NameProceedings of the American Control Conference
Volume2018-June
ISSN (Print)0743-1619

Other

Other2018 Annual American Control Conference, ACC 2018
CountryUnited States
CityMilwauke
Period6/27/186/29/18

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Sensor Selection via Observability Analysis in Feature Space'. Together they form a unique fingerprint.

Cite this