Title :
Active identification of stochastic dynamic systems
Author :
Abdenov, Amirza Zh
Author_Institution :
Novosibirsk Tech. State Univ.
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;
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
DOI :
10.1109/APEIE.1998.768984
