Bayesian recursive filtering with partially observed inputs and missing measurements

Jinya Su; Baibing Li; Wen Hua Chen

Bayesian recursive filtering with partially observed inputs and missing measurements

Jinya Su, Baibing Li, Wen Hua Chen

Computing Science

Loughborough University

Research output: Chapter in Book/Report/Conference proceeding › Published 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 language	English
Title of host publication	ICAC 2013 - Proceedings of the 19th International Conference on Automation and Computing
Subtitle of host publication	Future Energy and Automation
Publisher	IEEE Computer Society
Pages	2-7
Number of pages	6
ISBN (Print)	9781908549082
Publication status	Published - 2013
Event	19th International Conference on Automation and Computing, ICAC 2013 - London, United Kingdom Duration: 13 Sept 2013 → 14 Sept 2013

Conference

Conference	19th International Conference on Automation and Computing, ICAC 2013
Country/Territory	United Kingdom
City	London
Period	13/09/13 → 14/09/13

Keywords

Bayesian inference
Multiple missing measurements
Partially observed inputs
State estimation

Cite this

Bayesian recursive filtering with partially observed inputs and missing measurements. / Su, Jinya; Li, Baibing; Chen, Wen Hua.
ICAC 2013 - Proceedings of the 19th International Conference on Automation and Computing: Future Energy and Automation. IEEE Computer Society, 2013. p. 2-7 6662003.

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

Su, J, Li, B & Chen, WH 2013, Bayesian recursive filtering with partially observed inputs and missing measurements. in ICAC 2013 - Proceedings of the 19th International Conference on Automation and Computing: Future Energy and Automation., 6662003, IEEE Computer Society, pp. 2-7, 19th International Conference on Automation and Computing, ICAC 2013, London, United Kingdom, 13/09/13.

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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.",

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AB - 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.

KW - Bayesian inference

KW - Multiple missing measurements

KW - Partially observed inputs

KW - State estimation

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M3 - Published conference contribution

AN - SCOPUS:84892759565

SN - 9781908549082

SP - 2

EP - 7

BT - ICAC 2013 - Proceedings of the 19th International Conference on Automation and Computing

PB - IEEE Computer Society

T2 - 19th International Conference on Automation and Computing, ICAC 2013

Y2 - 13 September 2013 through 14 September 2013

ER -

Bayesian recursive filtering with partially observed inputs and missing measurements

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