multiple adaptive fading schmidt-kalman filter for unknown bias

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ID: 129718
2014
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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.
Reference Key
lou2014mathematicalmultiple Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Tai-Shan Lou;Zhi-Hua Wang;Meng-Li Xiao;Hui-Min Fu
Journal journal of power sources
Year 2014
DOI
10.1155/2014/623930
URL
http://dx.doi.org/10.1155/2014/623930
Keywords
civil engineering (general)mathematics engineering (general)

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