The resulting filter depends on how the transformed statistics of the UT are calculated and which set of sigma points are used. This means specifying the matrices, for each time-step k, following:
The Kalman filter model assumes the true state at time k is evolved from the state at (k−1) according to
where
At time k an observation (or measurement) zk of the true state xk is made according to
where
The initial state, and the noise vectors at each step {x0, w1, .

navigate here After reading the “Kalman Filter in one dimension” section, you should be familiar with the concepts of the Kalman Filter. The weights are calculated from the covariance, a measure of the estimated uncertainty of the prediction of the system’s state.

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Now, we shall predict the next state based on the initialization values. We get faster Kalman Filter convergence if we initialize with a more meaningful value.
The Kalman filtering equations provide an estimate of the state

x
basics

Full Article k

k

{\displaystyle {\hat {\mathbf {x} }}_{k\mid k}}

and its error covariance

P

k

k

{\displaystyle \mathbf {P} _{k\mid k}}

recursively. .

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