Multiple Adaptive Fading Schmidt-Kalman Filter for Unknown Bias. (24th November 2014)

• Record Type: Journal Article
• Authors: Lou, Tai-Shan; Wang, Zhi-Hua; Xiao, Meng-Li; Fu, Hui-Min
• Other Names: Wu Zheng-Guang Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2014(2014)
• Publication Date: 2014-11-24
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2014/623930
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Unknown biases in dynamic and measurement models of the dynamic systems can bring greatly negative effects to the state estimates when using a conventional Kalman filter algorithm. Schmidt introduces the "consider" analysis to account for errors in both the dynamic and measurement models due to the unknown biases. Although the Schmidt-Kalman filter "considers" the biases, the uncertain initial values and incorrect covariance matrices of the unknown biases still are not considered. To solve this problem, a multiple adaptive fading Schmidt-Kalman filter (MAFSKF) is designed by using the proposed multiple adaptive fading Kalman filter to mitigate the negative effects of the unknown biases in dynamic or measurement model. The performance of the MAFSKF algorithm is verified by simulation.