Kalman filtering

Kalman filtering is a mathematical technique used to estimate the true state of a system—like position or speed—by combining noisy measurements over time. It predicts the next state based on current knowledge, then updates this estimate using actual measurements, accounting for uncertainties in both. This process helps create a smooth, accurate understanding of the system's behavior, even when data is imperfect or incomplete. Widely used in navigation, robotics, and tracking, Kalman filters efficiently fuse information to provide real-time, reliable estimates in dynamic environments.