úδ’None(>KLN4The model for the extended Kalman filter is given by¼ \begin{aligned} \boldsymbol{x}_i &= \boldsymbol{a}(\boldsymbol{x}_{i-1}) + \boldsymbol{\psi}_{i-1} \\ \boldsymbol{y}_i &= {H}\boldsymbol{x}_i + \boldsymbol{\upsilon}_i \end{aligned} where\boldsymbol{a}9 is some non-linear vector-valued state update function.\boldsymbol{\psi}_{i}‡ are independent identically normally distributed random variables with mean 0 representing the fact that the state update is noisy: )\boldsymbol{\psi}_{i} \sim {\cal{N}}(0,Q).{H}G is a matrix which represents how we observe the hidden state process.\boldsymbol{\upsilon}_i… are independent identically normally distributed random variables with mean 0 represent the fact that the observations are noisy: -\boldsymbol{\upsilon}_{i} \sim {\cal{N}}(0,R).lWe assume the whole process starts at 0 with our belief of the state (aka the prior state) being given by =\boldsymbol{x}_0 \sim {\cal{N}}(\boldsymbol{\mu}_0, \Sigma_0)  Prior mean \boldsymbol{\mu}_0Prior variance \Sigma_0Observation map HObservation noise RState update function \boldsymbol{a}WA function which returns the Jacobian of the state update function at a given point 7\frac{\partial \boldsymbol{a}}{\partial \boldsymbol{x}} State noise QList of observations \boldsymbol{y}_i&List of posterior means and variances ((\hat{\boldsymbol{x}}_i, \hat{\Sigma}_i)Kalma_JzkbL9PZr508EzF0w73zeIKalman extKalmaninv