文章基本信息

标题：Extended State based Kalman Filter for Uncertain Systems with Bias ⁎
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作者：Xiaocheng Zhang ; Wenchao Xue ; Haitao Fang 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：2
页码：2299-2304
DOI：10.1016/j.ifacol.2020.12.018
语种：English
出版社：Elsevier
摘要：AbstractThis paper addresses the state estimation for a class of stochastic systems with both uncertain dynamics and measurement bias. By using the idea of uncertainty/disturbance estimation, an extended state based Kalman filter algorithm is developed to estimate the original state, the uncertain dynamics and the measurement bias. Furthermore, a necessary and sufficient condition for the observability of augmented system is presented. Also, the stability of the proposed algorithm is analyzed. It is shown that the proposed filter can achieve unbiased estimation of measurement bias, such that the influence of measurement bias is eliminated. Finally, a simulation study is provided to illustrate the effectiveness of proposed method.
关键词：KeywordsExtended state observerKalman filterUncertain estimationMeasurement bias