EFFECTIVE RADAR TRACKING USING ADAPTIVE KALMAN FILTER

Radar trailing plays an important role inside the space of early warning and detection system, whose preciseness is closely connected with filtering rule. With the event of noise jam technology in sign, linear filtering becomes extra and harder to satisfy the strain of measuring device trailing, whereas nonlinear filtering can solve problems like non - Gaussian noises. There exist several nonlinear filtering algorithms at the current, and their characteristic of linear and nonlinear data filters are totally different, we tend to discover that KF is easy to implement and has been wide used.Therefore, we'll simulate and show the performance of the Kalman data filter (KF) . One of the problems with the Kalman filter is that they will not sturdy against modeli ng uncertainties. The Kalman filter algorithmic rule is that the optimum filter for a system whereas not uncertainties. The performance of a Kalman filter is additionally significantly degraded if the actual system model does not match the model thereon th e Kalman filter was based, thus needed a advance version of Kalman filt er , This filter is thought as A djustive /Adaptive Kalman Filter (AKF).