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.
Compare with: Wiener filter