Howtosignalprocessing
- What is error covariance Kalman filter?
- What is error covariance?
- Why is extended Kalman filter not optimal?
- What is covariance EKF?
What is error covariance Kalman filter?
The Kalman Filter (KF) is a recursive scheme that propagates a current estimate of a state and the error covariance matrix of that state forward in time. The filter optimally blends the new information introduced by the measurements with old information embodied in the prior state with a Kalman gain matrix.
What is error covariance?
The error covariance matrix (ECM) is a dataset that specifies the correlations in the observation errors between all possible pairs of vertical levels. It is given as a two-dimensional array, of size NxN , where N is the number of vertical levels in the sounding data products.
Why is extended Kalman filter not optimal?
EKF is not optimal (mostly)
This happens because the EKF approximates state transitions and measurements using linear Taylor expansions, making the goodness of the approximation dependant on the degree of nonlinearity of the functions being approximated and on the uncertainty of its Gaussian belief [2][5].
What is covariance EKF?
The extended Kalman filter (EKF) is a popular state estimation method for nonlinear dynamical models. The model error covariance matrix is often seen as a tuning pa- rameter in EKF, which is often simply postulated by the user.
