Open AccessVariable step size predictor design for a class of linear discrete-time censored system
Author(s) -
Zhifang Li,
Haiquan Zhao,
Hailong Meng,
Yong Chen
Publication year - 2021
Publication title -
aims mathematics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.329
H-Index - 15
ISSN - 2473-6988
DOI - 10.3934/math.2021614
Subject(s) - mathematics , statistics , design matrix , trace (psycholinguistics) , variance (accounting) , kalman filter , variable (mathematics) , matrix (chemical analysis) , linear regression , mathematical analysis , philosophy , linguistics , accounting , business , materials science , composite material
We propose a novel variable step size predictor design method for a class of linear discrete-time censored system. We divide the censored system into two parts. The system measurement equation in one part doesn't contain the censored data, and the system measurement equation in the other part is the censored signal. For the normal one, we use the Kalman filtering technology to design one-step predictor. For the one that the measurement equation is censored, we determine the predictor step size according to the censored data length and give the gain compensation parameter matrix $β(\mathfrak{s})$ for the case predictor with obvious errors applying the minimum error variance trace, projection formula, and empirical analysis, respectively. Finally, a simulation example shows that the variable step size predictor based on empirical analysis has better estimation performance.