Comparative study of several nonlinear stochastic estimators

Asad Azemi, Edwin Engin Yaz

Research output: Contribution to journalConference articlepeer-review

11 Scopus citations


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
StatePublished - Dec 1 1999
EventThe 38th IEEE Conference on Decision and Control (CDC) - Phoenix, AZ, USA
Duration: Dec 7 1999Dec 10 1999

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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