Bridging a Gap in Applied Kalman Filtering: Estimating Outputs When Measurements Are Correlated with the Process Noise [Focus on Education]
Bridging a Gap in Applied Kalman Filtering: Estimating Outputs When Measurements Are Correlated with the Process Noise [Focus on Education]
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弥合应用卡尔曼滤波的差距：当测量值与过程噪声相关时估计输出[聚焦教育]
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发表时间：
2017
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通讯作者：
A. Deshpande
中科院分区：
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作者：
A. Deshpande
卡尔曼滤波的传统表述侧重于状态的估计而不是输出。感兴趣的输出可能包含来自已知输入u和未知输入w的馈通，yn=Cxn+Dun+Hwn。通常假设后验状态估计和已知输入足以产生最小方差输出估计，这是在大多数流行的控制设计工具箱中实现的相同方程。
Traditional statements of the Kalman filter focus on the estimation of states rather than outputs. An output of interest may contain feedthrough from both known inputs u and unknown inputs w, yn=Cxn+Dun+Hwn. It is usually assumed that the posterior state estimate and known inputs are enough to generate the minimum-variance output estimate, given by which is the same equation implemented in most popular control-design toolboxes.