Kalman filter based target tracking for track while scan data processing

The targets parameter to be measured for tracking are its relative position in range, azimuth angle, elevation angle and velocity. These parameters can be measured by tracking radar systems. Upon keeping the tracking of these measured parameters the tracker predict their future values. Fire control and missile guidance can be assisted through target tracking only. In fact missile guidance cannot be achieved without tracking the target properly. To predict target parameters (future samples) between scans, track while scan radar system sample each target once per scan interval by using sophisticated smoothing and prediction filters among which alpha-beta-gamma (αβγ) and Kalman filters are commonly used. The principle of recursive tracking and prediction filters are proposed in this paper for two maneuvering targets (lazy and aggressive maneuvering), by implementing the second and third order one dimensional fixed gain polynomial filter trackers. Finally the equations for an n-dimensional multi state Kalman filter are implemented and analyzed. In order to evaluate the performance of tracking filters the target considered in this paper is a Novator K100 Indian/Russian air-to-air missile designed to fly at Mach 4. In this paper the main objective of developing these filter tracking algorithmsis to reduce the measurement noise and tracking filter must be capable of tracking maneuvering targets with small residual (tracking errors).