Title :
Comparative study of several nonlinear stochastic estimators
Author :
Azemi, Asad ; Yaz, Edwin Engin
Author_Institution :
Dept. of Electr. Eng., Pennsylvania State Univ., University Park, PA, USA
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
Keywords :
Kalman filters; Lyapunov methods; Riccati equations; computational complexity; convergence; covariance analysis; filtering theory; mean square error methods; nonlinear estimation; nonlinear systems; state estimation; stochastic systems; Lyapunov-based filter; absolute error; computational time; convergence property; covariance assignment filter; design difficulty; extended Kalman filter; first-order nonlinear stochastic systems; mean square error; nonlinear stochastic estimators; second-order nonlinear stochastic systems; state estimation; state-dependent Riccati equation estimator; Convergence; Estimation error; Nonlinear equations; Nonlinear systems; Observers; Riccati equations; State estimation; Stochastic processes; Stochastic systems; Symmetric matrices;
Conference_Titel :
Decision and Control, 1999. Proceedings of the 38th IEEE Conference on
Conference_Location :
Phoenix, AZ
Print_ISBN :
0-7803-5250-5
DOI :
10.1109/CDC.1999.833259
