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题名: A novel adaptive unscented Kalman filter for nonlinear estimation
作者: Jiang Z(姜哲) ; Song Q(宋崎) ; He YQ(何玉庆) ; Han JD(韩建达)
作者部门: 机器人学研究室
会议名称: 46th IEEE Conference on Decision and Control
会议日期: December 12-14, 2007
会议地点: New Orleans, LA
会议主办者: IEEE Control Syst Soc, United Technologies, Springer, Wiley Blackwell, Taylor & Francis, Princeton Univ Press, Maplesoft, Siam
会议录: PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-14
会议录出版者: IEEE
会议录出版地: NEW YORK
出版日期: 2007
页码: 5805-5810
收录类别: CPCI(ISTP) ; EI
ISSN号: 0191-2216
ISBN号: 978-1-4244-1497-0
摘要: The normal unscented Kalman filter (UKF) suffers from performance degradation and even divergence while mismatch between the noise distribution assumed to be known as a priori by UKF and the true ones in a real system. In this paper, a novel adaptive UKF (AUKF) is developed and applied to nonlinear joint estimation of both time-varying states and parameters. A cost function is built based on the error between the covariance matrices of innovation and their corresponding estimations. An adaptive algorithm is then designed to online update the covariance of the process noise by minimizing the cost function. The updated covariance is further fed back into the normal UKF. As a result of using such an adaptive mechanism, the robustness of conventional UKF is substantially improved with respect to the uncertain and time-varying noise covariance in the real system. To illustrate this mechanism, simulations are conducted on the dynamics of an unmanned helicopter by jointly estimating both the states and model errors. The improvements of the proposed AUKF are demonstrated by comparing the results with and without the adaptive mechanism.
语种: 英语
产权排序: 1
内容类型: 会议论文
URI标识: http://ir.sia.cn/handle/173321/8499
Appears in Collections:机器人学研究室_会议论文

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Recommended Citation:
姜哲; 宋崎; 何玉庆; 韩建达.A novel adaptive unscented Kalman filter for nonlinear estimation.见:IEEE .PROCEEDINGS OF THE 46TH IEEE CONFERENCE ON DECISION AND CONTROL, VOLS 1-14,NEW YORK,2007,5805-5810
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