Tracking Algorithm of Hypersonic Vehicle Based on Two-Factor Enhancement

Abstract

针对临近空间高超声速飞行器飞行速度快、机动性能好以及轨迹复杂多变等特点，推导了基于双星观测系统的目标几何定位算法。结合目标飞行器的定位信息，提出了基于双因子加强的时间相关模型(Singer)-无迹卡尔曼滤波(UKF)高超声速飞行器跟踪算法，该算法通过引入调节因子mk，nk ，加强了滤波器的跟踪性能并且减小了状态滤波的初始值误差以及状态方程扰动误差对滤波结果的影响。仿真结果表明，对比于传统算法，改进后的基于Singer运动模型的自适应无迹卡尔曼跟踪滤波算法具有较好的跟踪精度和稳定性能。最后利用滤波估计值以及函数拟合的方式，进一步预测目标飞行器的运动轨迹。 Aiming at the characteristics of hypersonic vehicle in the near space, such as fast flight speed, good maneuverability and complex and variable trajectory, a geometric positioning algorithm for targets based on the dual star observation system is deduced. Combined with the positioning information of the hypersonic vehicle, a dual-factor enhanced Singer-UKF hypersonic vehicle tracking algorithm is proposed. By introducing two regulating factors mk，nk , the tracking per-formance of the filter is enhanced and the initial error of the state filter and the influence of the disturbance error of the state equation on the filtering result are reduced. The simulation re-sults show that the improved adaptive unscented Kalman tracking filtering algorithm based on the Singer motion has better tracking accuracy and stable performance compared with the tra-ditional algorithm. Finally, the filtering estimation value and the method of function fitting are used to further predict the motion trajectory of the hypersonic vehicle.