Comparative study of several nonlinear stochastic estimators

Asad Azemi, Edwin Engin Yaz

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

In this paper we investigate the relative performance and design procedures of several nonlinear stochastic estimators. The filters that we are comparing are: Lyapunov-Based, Covariance Assignment, Extended Kalman Filter, and State-Dependent Riccati Equation Estimator. First we provide an overview of these estimators and then we will compare their performance using first-and second-order nonlinear stochastic systems. The discussion will include convergence property, difficulty level of the design, computational time, and overall performance, based on absolute error and mean square error of the estimation.

Original languageEnglish (US)
Pages (from-to)4549-4554
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Volume5
StatePublished - 1999

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Comparative Study
Estimator
Riccati equations
Stochastic systems
Extended Kalman filters
Mean square error
Nonlinear Stochastic Systems
Riccati Equation
Convergence Properties
Kalman Filter
Lyapunov
Assignment
Filter
First-order
Dependent
Design

All Science Journal Classification (ASJC) codes

  • Chemical Health and Safety
  • Control and Systems Engineering
  • Safety, Risk, Reliability and Quality

Cite this

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Comparative study of several nonlinear stochastic estimators. / Azemi, Asad; Yaz, Edwin Engin.

In: Proceedings of the IEEE Conference on Decision and Control, Vol. 5, 1999, p. 4549-4554.

Research output: Contribution to journalArticle

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