Kalmansmoothing
Kalman smoothing refers to a family of algorithms for estimating the complete sequence of a dynamic system’s hidden states from noisy observations, by using all available measurements. It is typically used in offline analysis, where data has already been collected, in contrast to the online Kalman filter which updates estimates in real time.
In the standard linear Gaussian setting, the system is modeled with a state equation x_{k+1} = F_k
A common approach is the Rauch–Tung–Striebel (RTS) smoother. It starts by running the forward Kalman filter to
Variants exist for nonlinear models, including extended Kalman smoothers, unscented smoothers, and particle smoothers. Kalman smoothing