| 随机系数矩阵卡尔曼滤波 |
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| 引用本文： | 罗丹丹,朱允民.随机系数矩阵卡尔曼滤波[J].四川大学学报(自然科学版),2008,45(6):1309-1312. |
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| 作者姓名： | 罗丹丹 朱允民 |
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| 作者单位： | 四川大学数学学院,成都,610064 |
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| 基金项目： | “863”项目(2006AA12A104) |
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| 摘 要： | 作者考虑了状态转移矩阵和量测矩阵是随机阵的线性离散时间动态系统的状态的线性最小方差递推估计问题，即随机系数矩阵卡尔曼滤波，说明了该系统可化为过程噪声和量测噪声均依赖于状态，而转移矩阵和量测矩阵是非随机阵的线性动态系统，从而证明了新系统的状态的最小方差估计问题仍有卡尔曼滤波形式.
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| 关 键 词： | 随机系数矩阵 随机卡尔曼滤波 线性最小方差递推估计 |
Random parameter matrices Kalman filtering |
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| LUO Dan-Dan,ZHU Yun-Min.Random parameter matrices Kalman filtering[J].Journal of Sichuan University (Natural Science Edition),2008,45(6):1309-1312. |
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| Authors: | LUO Dan-Dan ZHU Yun-Min |
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| Institution: | School of Mathematics, Sichuan University;School of Mathematics, Sichuan University |
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| Abstract: | The Linear Minimum Variance recursive state estimation is studied in the linear discrete time dynamic system with random state transition and observation matrices, i.e., random parameter matrices Kalman filtering. It is shown that such system can be reduced to a linear dynamic system with deterministic parameter matrices and state-depending process and measurement noises, and the Linear Minimum Variance recursive state estimation of the new system is still in the form of Kalman filtering. |
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| Keywords: | random parameter matrices random Kalman filtering linear mininum variance recursive state stimation |
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