Kalman filter is often used tool for stochastic state estimation of a noisy system that is described by linear system and linear measurement model. It uses all it gets to make an overall best estimate of a state, i.e., the values of the variables of interest. It does not matter how accurate or precise the information is. It does this by incorporating knowledge about the system dynamics, statistical descriptions about the system dynamics, statistical descriptions of the system noise, measurement

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... noise, uncertainty in the dynamics model and any available information about the initial conditions of the variables of the interest. We discuss the estimation of maneuvering aircraft state vector (i.e., position, velocity, acceleration) using Kalman filter (KF) and its variants like Extended Kalman filter (EKF) and Unscented Kalman filter (UKF).The tracking of an aircraft is challenging and highly complex in non-linear filtering. The major challenge lies in keeping track of the aircraft, whose dynamics deviate due to its evasive maneuvering capability. The Kalman-based estimator is the best linear minimum mean squared error (MMSE) available today under Gaussian assumptions. But, for tracking a non-linear system, such as aircraft tracking, the EKF is used because of nonlinear nature in motion.