Unscented Transform

The Unscented Transform is a mathematical technique used to estimate how uncertainty in input data affects the output of a nonlinear system. Instead of approximating the system with linear assumptions, it intelligently samples a set of carefully chosen points (called sigma points) around the mean, then propagates these points through the nonlinear function. By analyzing the transformed points, it accurately predicts the mean and variance of the output, capturing the effects of nonlinearity more precisely than simple linear methods. This approach is common in advanced estimation algorithms like the Unscented Kalman Filter.