Cubature Kalman Filter Based Multi-sensor Data Fusion Algorithm for Maneuvering Target Tracking

To improve the accuracy of ballistic target tracking, a multi-sensor data fusion algorithm based on Cubature Kalman filter is introduced in this paper. Firstly, the dynamic model of a midcourse ballistic target is established via force analysis of the flight in the Earth Centered Inertial (ECI) frame. The model is then transformed to the local East-North-Up (ENU) frame via coordinate transformation. Secondly, one certain sensor is selected as the information fusion center, and a centralized measurement model is constructed through multi-sensor measurement augment in the ENU frame. Finally, based on the state-space equation and observation equation established previously, a Cubature Kalman filter algorithm is designed to obtain the real-time state estimation of the target, and the tracking effects as well as the algorithm performance are evaluated through Monte Carlo simulations. The results of simulation show that the proposed algorithm can achieve high precision and stable tracking performance of the target.