Extended Kalman Filter (EKF) Linearization of Non Linear Functions I have more general question about the extended Kalman filter usage. What is not clear to me is why the EKF uses non-linear functions $f$ and $h$ for state prediction and estimate, while in other places the Jacobian of these functions is used. 

Why the following is never used? 

First calculate the linearized state and measurement models at previous estimate point using Jacobian. Use the linearized state transition and measurements matrix everywhere instead of non-linear in this specific iteration.
We can't use the initial linearized-model of non-linear system again and again in each iteration because linear-model approximates the nonlinear model only around some small neighbourhood of the starting point. In other words, linear model is good(i-e approximates the nonlinear model) only around some small-interval around the point. As, we need to estimate the current state again and again, if we don't linearlize the non-linear model around the current estimate in each iteration, we would we getting worse and worse (in terms of linear model) and consequently we will no longer correctly estimate the state.