Efficient RADAR Tracking Using Adaptive Kalman Filter

Radar tracking plays a crucial role within the space of early warning and detection system, whose preciseness is closely connected with filtering rule. There are various nonlinear filtering algorithms at the present, owning their explicit characteristics. Through the analyses of linear and nonlinear information filters, we discover that KF is simple to implement and has been wide used. Therefore, we'll simulate and show the performance of the Kalman information filter (KF). One of the issues with the Kalman filter is that they'll not strong against modeling uncertainties. The Kalman filter algorithm is that the optimum filter for a system while not uncertainties. The performance of a Kalman filter is also considerably degraded if the particular system model doesn't match the model on that the Kalman filter was primarily based, therefore required an advance version of Kalman filter , This filter is known as Adaptive Kalman Filter (AKF). Kalman filter (KF) is mostly applicable for linear system i.e. where speed are linear but Radar movements are nonlinear and they cannot easily track with linear system and estimation in Radar system is also not easy, so we can say that Kalman filter is not efficient where delay or uncertainties are present ,So we required a new filter they work efficiently in those condition , we use Adaptive Kalman filter (AKF).