The unscented transform (UT) is a mathematical function used to estimate the result of applying a given nonlinear transformation to a probability distribution that is characterized only in terms of a finite set of statistics. The most common use of the unscented transform is in the nonlinear projection of mean and covariance estimates in the context of nonlinear extensions of the
Kalman filter
For statistics and control theory, Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, including statistical noise and other inaccuracies, and produces estimat ...
. Its creator
Jeffrey Uhlmann
Jeffrey K. Uhlmann is an American research scientist who is probably best known for his mathematical generalizations of the Kalman filter. Most of his publications and patents have been in the field of data fusion. He is also known for being a cult ...
explained that "unscented" was an arbitrary name that he adopted to avoid it being referred to as the “Uhlmann filter” though others have indicated that "unscented" is a contrast to "scented" intended as a euphemism for "stinky"
Background
Many filtering and control methods represent estimates of the state of a system in the form of a mean vector and an associated error covariance matrix. As an example, the estimated 2-dimensional position of an object of interest might be represented by a mean position vector,