Kalman filtering based gradient estimation algorithms for observer canonical state-space systems with moving average noises. Issue 10 (July 2019)

• Record Type: Journal Article
• Authors: Cui, Ting; Ding, Feng; Li, Xiangli; Hayat, Tasawar
• Is Part Of: Journal of the Franklin Institute. Volume 356:Issue 10(2019)
• Page Start: 5485
• Page End: 5502
• Publication Date: 2019-07
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2018.12.031
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper focuses on the joint parameter and state estimation issue for observer canonical state-space systems with white noises in state equations and moving average noises in output equations. By means of the Kalman filtering and the gradient search, we derive a Kalman filtering based extended stochastic gradient algorithm. For purpose of achieving the higher parameter estimation accuracy, a Kalman filtering based multi-innovation extended stochastic gradient algorithm is proposed on the basis of the multi-innovation identification theory. Finally, the effectiveness of the proposed algorithms is validated through a numerical example.