Learning context-awaremeasurementmodels

Nurali Virani, Ji Woong Lee, Shashi Phoha, Asok Ray

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

This paper presents machine learning-based measurement models with state-augmenting contexts as a paradigm of dynamic data-driven application systems (DDDAS). In order to formulate well-posed statistical inference problems in realistic scenarios, one needs to identify and take into account all environmental factors and ambient conditions, called contexts, which affect sensor measurements. A kernel-based mixture modeling method carries out this task in an unsupervised manner, and results in a machine-defined context set and a probability distribution on it. The resulting measurement model is guaranteed to have contextual awareness, in the sense that the measurements are mutually independent conditioned on the system state and context. Numerical examples illustrate how contextual awareness improves inference performance in the setting of sequential target detection.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4491-4496
Number of pages6
Volume2015-July
ISBN (Electronic)9781479986842
DOIs
StatePublished - Jan 1 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Other

Other2015 American Control Conference, ACC 2015
CountryUnited States
CityChicago
Period7/1/157/3/15

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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