Joint state and multi-innovation parameter estimation for time-delay linear systems and its convergence based on the Kalman filtering

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• 作者：Feng Ding^a ; ^b ; ^{fding@jiangnan.edu.cn}Author Vitae ; Xuehai Wang^b ; ^cAuthor Vitae ; Li Mao^bAuthor Vitae ; Ling Xu^bAuthor Vitae
• 关键词：Parameter estimation ; Kalman filtering ; Multi-innovation identification ; Time-delay system
• 刊名：Digital Signal Processing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：62
• 期：Complete
• 页码：211-223
• 全文大小：624 K
• 卷排序：62

文摘

This paper studies the joint state and parameter estimation problem for a linear state space system with time-delay. A multi-innovation gradient algorithm is developed based on the Kalman filtering principle. To improve the convergence rate, a filtering based multi-innovation gradient algorithm is proposed by using the filtering technique. The analysis indicates that the parameter estimates given by the proposed algorithms converge to their true values under the persistent excitation conditions. A simulation example is given to confirm that the proposed algorithms are effective.