Bayesian recursive filtering with partially observed inputs and missing measurements

Jinya Su, Baibing Li, Wen Hua Chen

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

2 Citations (Scopus)

Abstract

In this paper, the problem of state estimation is considered for discrete-time stochastic linear systems subject to both partially observed inputs and multiple missing sensor measurements. First, the partially available information on the unknown inputs and the state equation are used to form the prior distribution of the state vector at each step. To obtain an analytically tractable likelihood function, the effect of missing measurements is broken down and the associated uncertainty is modeled as part of the measurement noise. A recursive optimal filter is obtained using Bayes' rule. Finally, a numerical example is provided to evaluate the effectiveness of the developed method.

Original languageEnglish
Title of host publicationICAC 2013 - Proceedings of the 19th International Conference on Automation and Computing
Subtitle of host publicationFuture Energy and Automation
PublisherIEEE Computer Society
Pages2-7
Number of pages6
ISBN (Print)9781908549082
Publication statusPublished - 2013
Event19th International Conference on Automation and Computing, ICAC 2013 - London, United Kingdom
Duration: 13 Sep 201314 Sep 2013

Conference

Conference19th International Conference on Automation and Computing, ICAC 2013
Country/TerritoryUnited Kingdom
CityLondon
Period13/09/1314/09/13

Keywords

  • Bayesian inference
  • Multiple missing measurements
  • Partially observed inputs
  • State estimation

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