基于改进IMM-UKF的弹道导弹再入段跟踪数据滤波?

针对弹道系数未知的弹道导弹再入段跟踪雷达测量数据滤波这类非线性强的滤波问题，提出可变多模型无迹卡尔曼滤波算法。利用无迹卡尔曼滤波逼近精度高，计算量小，适应于任意非线性模型的特点，将其作为多模型的基本滤波器；滤波算法根据各模型正确描述目标状态的概率，动态地改变多模型数量和模型参数。上述方法的综合运用，提高对目标状态估计精度，降低了计算的复杂度，仿真实验验证了方法的有效性。

Reentry-Phase Tracking of Unknown Ballistic Missiles based on improved IMM-UKF

Radar tracking of a ballistic target in re-entry phase with unknown ballistic coefficient is a problem of nonlinear estimation, a variable-structure multiple mode based Unscented Kalman filter has been proposed for it. UKF has been used as the basic filter for its more accurate, easier to implement, and uses the same order of calculations as linearization. The mode coefficients and IMM mode set is adaptively changed and then subsumes the true dynamic model according to the real time estimation of object state. Simulation shows it improves tracking precision effectively and makes computational complexity lower.