Ballistic target tracking using multiple model Kalman filter with a priori ballistic information
Fırat Kumru, Tamer Akça, Emre Altuntas · 2017
In order to increase the tracking performance of ballistic targets, various estimation algorithms have been implemented in the literature. Extended Kalman Filter is one of the most widely used estimation algorithm which uses the nonlinear system and measurement models and linearization methods to estimate the state and state covariances. In the first part of this study, a ballistic coefficient state is augmented to the position and velocity states in an Extended Kalman Filter in order to estimate the target kinematics and identify the target ballistics. Since the observability of the ballistic coefficient state is weak, sufficient accuracy in the impact point estimation can not be obtained. In the proposed method, different sets of target ballistic coefficients with respect to varying target speed is known as a priori information. Using this a priori information, a multiple model Extended Kalman Filter which estimates only the kinematic states is utilized and aerodynamic forces acting on the ballistic target is accounted for in the state propagation. Each of the multiple model filters uses different a priori ballistic information set and likelihood of each filter is calculated in order to select one of the model and predict the impact point of the ballistic target.