States and parameters estimation for biomass substrate hypothetical system

Imen Baklouti, Majdi Mansouri, Mohamed Nounou, Nouha Jaoua, Ahmed Ben Hamida

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

Abstract

To overcome the problem of uncertainty in the environmental models, we are focused on the difficulty of, the cost related with, getting the measurements, of dual state and/or parameter estimates. This paper, presents an Iterated Square-Root Central Difference Kalman Particle Filter (ISRCDKF-PF) extension which is suggested for the estimation of the joint state and parameters in environmental systems. Amongst the different Byesian techniques, are compared and calculated for the estimation performance, called the conventional of the Square-Root Central Difference Kalman Filter (SRCDKF), the Iterated Square-Root Central Difference Kalman Filter (ISRCDKF), the Particle Filter (PF), the Square-Root Central Difference Kalman Particle Filter (SRCDK-PF) and the Iterated Square-Root Central Difference Kalman Particle Filter (ISRCDKF-PF). The proposed approach consists of a PF based on ISRCDKF to exceed the standard Particle Filter by delivering more accuracy state and parameter estimations. The proposal distribution incorporates the latest observation in system state transition density, so it may well match the a posteriori density. The estimation performance of the proposed Bayesian methods, namely the Square-Root Central Difference Kalman Filter (SRCDKF), the Iterated Square-Root Central Difference Kalman Filter (ISRCDKF), the Particle Filter (PF), the Square-Root Central Difference Kalman Particle Filter (SRCDKF-PF) and the Iterated Square-Root Central Difference Kalman Particle Filter (ISRCDKF-PF) are compared by measuring the Root Mean Square Error (RMSE) and respect to the noise-free data. The results reveal that the ISRCDKF-PF extension provides a significant improvement and a better estimation accuracy than the SRCDKF, ISRCDKF, PF and SRCDKF-PF techniques.

Original languageEnglish
Title of host publication2nd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages567-570
Number of pages4
ISBN (Electronic)9781467385268
DOIs
Publication statusPublished - 26 Jul 2016
Event2nd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2016 - Monastir, Tunisia
Duration: 21 Mar 201624 Mar 2016

Other

Other2nd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2016
CountryTunisia
CityMonastir
Period21/3/1624/3/16

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Keywords

  • Environmental system
  • Iterated Square-Root Central Difference Kalman Filter
  • Particle Filter

ASJC Scopus subject areas

  • Signal Processing

Cite this

Baklouti, I., Mansouri, M., Nounou, M., Jaoua, N., & Ben Hamida, A. (2016). States and parameters estimation for biomass substrate hypothetical system. In 2nd International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2016 (pp. 567-570). [7523145] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ATSIP.2016.7523145