A robust data assimilation approach in the absence of sensor statistical properties

Reza Madankan, Puneet Singla, Tarunraj Singh

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

1 Citation (Scopus)

Abstract

A convex optimization based approach is presented to perform model-data assimilation of spatial temporal dynamical systems where sensor error characteristics are not available. The key idea of the proposed technique is that one should not make any assumption regarding the statistical properties of sensor data when they are not available. Recently developed quadrature scheme, Conjugate Unscented Transformation in conjunction with convex optimization tools is used to obtain an approximation of posterior density function. The proposed approach is validated by considering the problem of source parameter estimation for toxic material release in the atmosphere. The numerical experiments provides a basis for optimism for the robustness of the proposed methodology.

Original languageEnglish (US)
Title of host publicationACC 2015 - 2015 American Control Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5206-5211
Number of pages6
ISBN (Electronic)9781479986842
DOIs
StatePublished - Jul 28 2015
Event2015 American Control Conference, ACC 2015 - Chicago, United States
Duration: Jul 1 2015Jul 3 2015

Publication series

NameProceedings of the American Control Conference
Volume2015-July
ISSN (Print)0743-1619

Other

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

Fingerprint

Convex optimization
Toxic materials
Sensors
Parameter estimation
Probability density function
Dynamical systems
Experiments

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Madankan, R., Singla, P., & Singh, T. (2015). A robust data assimilation approach in the absence of sensor statistical properties. In ACC 2015 - 2015 American Control Conference (pp. 5206-5211). [7172152] (Proceedings of the American Control Conference; Vol. 2015-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2015.7172152
Madankan, Reza ; Singla, Puneet ; Singh, Tarunraj. / A robust data assimilation approach in the absence of sensor statistical properties. ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 5206-5211 (Proceedings of the American Control Conference).
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Madankan, R, Singla, P & Singh, T 2015, A robust data assimilation approach in the absence of sensor statistical properties. in ACC 2015 - 2015 American Control Conference., 7172152, Proceedings of the American Control Conference, vol. 2015-July, Institute of Electrical and Electronics Engineers Inc., pp. 5206-5211, 2015 American Control Conference, ACC 2015, Chicago, United States, 7/1/15. https://doi.org/10.1109/ACC.2015.7172152

A robust data assimilation approach in the absence of sensor statistical properties. / Madankan, Reza; Singla, Puneet; Singh, Tarunraj.

ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc., 2015. p. 5206-5211 7172152 (Proceedings of the American Control Conference; Vol. 2015-July).

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

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AB - A convex optimization based approach is presented to perform model-data assimilation of spatial temporal dynamical systems where sensor error characteristics are not available. The key idea of the proposed technique is that one should not make any assumption regarding the statistical properties of sensor data when they are not available. Recently developed quadrature scheme, Conjugate Unscented Transformation in conjunction with convex optimization tools is used to obtain an approximation of posterior density function. The proposed approach is validated by considering the problem of source parameter estimation for toxic material release in the atmosphere. The numerical experiments provides a basis for optimism for the robustness of the proposed methodology.

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Madankan R, Singla P, Singh T. A robust data assimilation approach in the absence of sensor statistical properties. In ACC 2015 - 2015 American Control Conference. Institute of Electrical and Electronics Engineers Inc. 2015. p. 5206-5211. 7172152. (Proceedings of the American Control Conference). https://doi.org/10.1109/ACC.2015.7172152