用于再入弹道目标跟踪的Sigma点卡尔曼滤波器（英文）

Sigma Point Kalman Filters for Re-entry Ballistic Target Tracking

提出一种新的Sigma点卡尔曼滤波器,求积分卡尔曼滤波器(Quadrature Kalman Filter,QKF),用于再入弹道目标的跟踪问题。新滤波器通过一系列参数化高斯密度的高斯-赫米特求积分点,使用统计线性回归的方法来线性化非线性函数的。仿真实验比较了这个新的Sigma点滤波器和扩展卡尔曼滤波器(Extended Kalman Filter,EKF),均差滤波器(Divided Difference Filter,DDF),无味滤波器(Uncented Kalman Filter,UKF)。结果表明所有Sigma点滤波器的估计误差都低于EKF的估计误差。QKF的估计误差低于UKF的估计误差,其滤波可靠性也与UKF很接近。QKF的计算复杂性比UKF稍高,新的Sigma点滤波器是一种有效算法。

A new kind of sigma-point Kalman filter was proposed, quadrature Kalman filter（QKF）, for the purpose of re-entry ballistic target tracking applications. The new filter linearized the nonlinear functions using statistical linear regression method through a set of Gaussian-Hermite quadrature points that parameterized the Gaussian density. The simulation experiment compared this new sigma point filter with EKF, DDF and UKF. Simulation results show that the estimation errors of all sigma point filters are all lower than that of EKF. The estimation error of QKF is lower than that of UKF, and its filtering credibility is almost same as that of UKF. The calculation complexity of QKF is a litter higher than that of UKF. The new sigma-point filter is an effective algorithm.