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Kalman filter | Matlab implements unscented kalman filter simulation
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Unscented Kalman filtering (UKF) does not linearize a nonlinear function. Unscented kalman still uses the architecture of linear kalman filtering. For one-step prediction equations, unscented transform (UT) is used to deal with the nonlinear transfer problem of mean and covariance. The UKF algorithm approximates the probability density distribution of a nonlinear function and uses a series of deterministic samples to approximate the posterior probability density of the state, rather than approximating the nonlinear function and does not require the derivation of the Jacobian matrix. UKF does not ignore high-order terms, so it has higher accuracy for nonlinear distribution statistics, effectively overcoming the shortcomings of low estimation accuracy and poor stability of extended Kalman filtering.
Unscented transformation (UT transformation)
The implementation principle of UT transformation is to pick points in the original distribution according to a certain rule, so that the mean and covariance state distribution of these points are equal to the mean and covariance of the original state distribution; these points are into the nonlinear function,