The Kalman filter (named after its inventor, Rudolf Kalman) is an efficient recursive computational solution for tracking a time-dependent state vector with noisy equations of motion in real time by the least-squares method. It is used to separate signal from noise so as to optimally predict changes in a modeled system with time.
Kalman filtering is used extensively in control systems engineering.
