如何直观地理解卡尔曼增益？
该卡尔曼滤波算法的工作原理如下 初始化和。x^0|0x^0|0 \hat{\textbf{x}}_{0|0}P0|0P0|0\textbf{P}_{0|0} 每次迭代k=1,…,nk=1,…,nk=1,\dots,n 预测 预测（先验）状态估计 预测（先验）估计协方差更新x^k|k−1=Fkx^k−1|k−1+Bkukx^k|k−1=Fkx^k−1|k−1+Bkuk \hat{\textbf{x}}_{k|k-1} = \textbf{F}_{k}\hat{\textbf{x}}_{k-1|k-1} + \textbf{B}_{k} \textbf{u}_{k} Pk|k−1=FkPk−1|k−1FTk+QkPk|k−1=FkPk−1|k−1FkT+Qk \textbf{P}_{k|k-1} = \textbf{F}_{k} \textbf{P}_{k-1|k-1} \textbf{F}_{k}^{\text{T}} + \textbf{Q}_{k} 创新或度量残差 创新（或残差）协方差 最佳 卡尔曼增益 更新（后验）状态估计 更新（后验）估计协方差 y~k=zk−Hkx^k|k−1y~k=zk−Hkx^k|k−1 \tilde{\textbf{y}}_k = \textbf{z}_k - \textbf{H}_k\hat{\textbf{x}}_{k|k-1}Sk=HkPk|k−1HTk+RkSk=HkPk|k−1HkT+Rk\textbf{S}_k = \textbf{H}_k \textbf{P}_{k|k-1} \textbf{H}_k^\text{T} + \textbf{R}_k Kk=Pk|k−1HTkS−1kKk=Pk|k−1HkTSk−1\textbf{K}_k = \textbf{P}_{k|k-1}\textbf{H}_k^\text{T}\textbf{S}_k^{-1}x^k|k=x^k|k−1+Kky~kx^k|k=x^k|k−1+Kky~k\hat{\textbf{x}}_{k|k} = \hat{\textbf{x}}_{k|k-1} + \textbf{K}_k\tilde{\textbf{y}}_kPk|k=(I−KkHk)Pk|k−1Pk|k=(I−KkHk)Pk|k−1\textbf{P}_{k|k} = (I - \textbf{K}_k …