Active identification of stochastic dynamic systems

Author

Abdenov, Amirza Zh

Author_Institution

Novosibirsk Tech. State Univ.

fYear

1998

fDate

1998

Firstpage

348

Lastpage

352

Abstract

It is necessary to know the covariance matrices of measurement noise and dynamic system noise, state and control matrices in order to estimate the optimal state vector. In this paper, algorithmic aspects of linear dynamic system active identification for optimal solution of the Kalman filter problem are considered. It is proposed to solve the input design task by using an input signal autocorrelation function in the time domain and an input signal spectral density in the frequency domain

Keywords

Kalman filters; correlation methods; covariance matrices; frequency-domain analysis; identification; signal processing; spectral analysis; state estimation; stochastic systems; time-domain analysis; Kalman filter problem; active identification; control matrices; covariance matrices; dynamic system noise; frequency domain; input signal autocorrelation function; input signal spectral density; linear dynamic system; measurement noise; optimal state vector; state matrices; stochastic dynamic systems; time domain; Autocorrelation; Control systems; Covariance matrix; Noise measurement; Optimal control; Signal design; State estimation; Stochastic resonance; Stochastic systems; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronic Instrument Engineering Proceedings, 1998. APEIE-98. Volume 1. 4th International Conference on Actual Problems of

Conference_Location

Novosibirsk

Print_ISBN

0-7803-4938-5

Type

conf

DOI

10.1109/APEIE.1998.768984

Filename

768984