Sigma Point Kalman Filter

The Sigma Point Kalman Filter is an advanced technique used in engineering and data analysis to estimate the state of a dynamic system affected by uncertainty. Unlike traditional Kalman filters, which assume linear behavior, the Sigma Point method handles nonlinear systems by selecting specific points (sigma points) that represent probability distributions. These points are propagated through the system's dynamics to better capture changes and uncertainties. This approach improves the accuracy of predictions in applications like robotics, navigation, and tracking, helping to make informed decisions even when data is noisy or incomplete.