Example Concept: The EKF extends the traditional Kalman filter to nonlinear systems by using a first-order Taylor expansion to linearize the system dynamics and measurement models around the current state estimate. This involves calculating the Jacobian matrices of the system and measurement functions, which are then used to propagate the state and covariance estimates. The EKF iteratively updates the state estimate by predicting the next state based on the current estimate and control inputs, then correcting it using the new measurements.