A novel Gaussian sum filter method for accurate solution to the nonlinear filtering problem

Gabriel Terejanu, Puneet Singla, Tarunraj Singh, Peter D. Scott

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

26 Citations (Scopus)

Abstract

The paper presents two methods of updating the weights of a Gaussian mixture to account for the density propagation within a data assimilation setting. The evolution of the first two moments of the Gaussian components is given by the linearized model of the system. When observations are available, both the moments and the weights are updated to obtain a better approximation to the a posteriori probability density function. This can be done through a classical Gaussian Sum Filter. When the measurement model offers little or no information in updating the states of the system, better estimates may be obtained by updating the weights of the mixands to account for the propagation effect on the probability density function. The update of the forecast weights proves to be important in pure forecast settings, when the frequency of the measurements is low, when the uncertainty of the measurements is large or the measurement model is ambiguous making the system unobservable. Updating the weights not only provides us with better estimates but also with a more accurate probability density function. The numerical results show that updating the weights in the propagation step not only gives better estimates between the observations but also gives superior performance for systems when the measurements are ambiguous.

Original languageEnglish (US)
Title of host publicationProceedings of the 11th International Conference on Information Fusion, FUSION 2008
StatePublished - Dec 1 2008
Event11th International Conference on Information Fusion, FUSION 2008 - Cologne, Germany
Duration: Jun 30 2008Jul 3 2008

Publication series

NameProceedings of the 11th International Conference on Information Fusion, FUSION 2008

Other

Other11th International Conference on Information Fusion, FUSION 2008
CountryGermany
CityCologne
Period6/30/087/3/08

Fingerprint

Nonlinear filtering
Probability density function

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

Cite this

Terejanu, G., Singla, P., Singh, T., & Scott, P. D. (2008). A novel Gaussian sum filter method for accurate solution to the nonlinear filtering problem. In Proceedings of the 11th International Conference on Information Fusion, FUSION 2008 [4632185] (Proceedings of the 11th International Conference on Information Fusion, FUSION 2008).
Terejanu, Gabriel ; Singla, Puneet ; Singh, Tarunraj ; Scott, Peter D. / A novel Gaussian sum filter method for accurate solution to the nonlinear filtering problem. Proceedings of the 11th International Conference on Information Fusion, FUSION 2008. 2008. (Proceedings of the 11th International Conference on Information Fusion, FUSION 2008).
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Terejanu, G, Singla, P, Singh, T & Scott, PD 2008, A novel Gaussian sum filter method for accurate solution to the nonlinear filtering problem. in Proceedings of the 11th International Conference on Information Fusion, FUSION 2008., 4632185, Proceedings of the 11th International Conference on Information Fusion, FUSION 2008, 11th International Conference on Information Fusion, FUSION 2008, Cologne, Germany, 6/30/08.

A novel Gaussian sum filter method for accurate solution to the nonlinear filtering problem. / Terejanu, Gabriel; Singla, Puneet; Singh, Tarunraj; Scott, Peter D.

Proceedings of the 11th International Conference on Information Fusion, FUSION 2008. 2008. 4632185 (Proceedings of the 11th International Conference on Information Fusion, FUSION 2008).

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

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Terejanu G, Singla P, Singh T, Scott PD. A novel Gaussian sum filter method for accurate solution to the nonlinear filtering problem. In Proceedings of the 11th International Conference on Information Fusion, FUSION 2008. 2008. 4632185. (Proceedings of the 11th International Conference on Information Fusion, FUSION 2008).