Non-Gaussian noise quadratic estimation for linear discrete-time time-varying systems

详细信息    查看全文

• 作者：Huihong Zhao^a ; ^{huihong1980@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Chenghui Zhang^b ; ^{zchui@sdu.edu.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Input noise quadratic polynomial estimation ; Kronecker algebra ; Deconvolution filter ; Fixed-lag smoother
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：22 January 2016
• 年：2016
• 卷：174
• 期：part_PB
• 页码：921-927
• 全文大小：423 K

文摘

This study deals with the input noise quadratic polynomial estimation problem for linear discrete-time non-Gaussian systems. The design of the non-Gaussian noise quadratic deconvolution filter and fixed-lag smoother is firstly converted into a linear estimation problem in a suitable second-order polynomial extended system. By employing the Kronecker algebra rules, the stochastic characteristics of the augmented noise in the augmented system are discussed. Then a solution to the non-Gaussian noise quadratic estimator is obtained through applying the projection formula in Kalman filtering theory. In addition, the stability is proved by constructing an equivalent state-space model with uncorrelated noises. Finally, a numerical example is given to show the effectiveness of the proposed method.