Nonlinear filters for tracking maneuverable ballistic missile targets on reentry

Abstract

With the development of the ballistic missile defense penetration technology, the reentry ballistic missile can be maneuverable to change its trajectory in order to achieve defense penetration. Besides the air resistance, the maneuvering force generated by missile causes the abrupt change of acceleration during the reentry duration, so in order to obtain better tracking performance, it's necessary to improve nonlinear filters' adaptability to such maneuver. The paper puts forward a self-adaptive EKF/UKF with attenuation memory maneuver detection based on the conventional EKF /UKF algorithm. When the ballistic missile targets do not change its trajectory on reentry, the filtering precision of UKF is higher than that of EKF, and meanwhile the prior information of the ballistic coefficient can improve the filtering precision and stability. To track the targets which can make trajectory-change for defense penetration, the standard EKF and the standard UKF both have large filtering deviation. However, through maneuver detection and adaptive adjustment of the process noise convariance matrix in time, the self-adaptive EKF/UKF can reduce the filtering error caused by the abrupt acceleration change evidently, and attain a much higher filtering precision and enhanced robustness. The simulation results validate this method.