Derivation of Unscented Kalman Filter (UKF)
Linearization of non-linear dynamics/observation in the EKF is a clever trick to use the KF equations. We approximate a new linear function from non-linear function around one point i.e. the mean of the Gaussian. However, there is still a better way to approximate it, say we approximate around multiple points. This is the inituition behind the powerful method called Unscented Kalman Filter (UKF). We take some points on source Gaussian and map them on target Gaussian after passing points through some non linear function and then we calculate the new mean and variance of transformed Gaussian. These special set of points are called "Sigma points". It can be very difficult to transform whole state distribution (computationally expensive) through a non linear function but it is very easy to transform some individual points. These sigma points are the representatives of whole distribution. Unscented Transform Consider a non-linear function $z = f(x)$ for a random variable $\math...