A reentry ballistic target tracking method is proposed based on spherical simplex radial kalman filtering algorithm which can effectively improve target tracking accuracy relied on ground-based radar. First, the nonlinear dynamical equations of reentry ballistic target and measurement equation are obtained on Earth-fixed coordinate system and the fourth order Runge-Kutta method is used to get discrete form. Then we approach nonlinear function's Gauss weighted integral according to Spherical Simplex-Radial and obtain Spherical Simplex-Radial Cubature Kalman Filter(SSRCKF) based on Bayesian filter theory. The simulation result proves that SSRCKF can achieve higher estimation accuracy compared with CKF, the localization accuracy of SSRCKF is improved by 4.5m, and velocity accuracy is improved by 0.06m/s.%提出一种再入弹道目标跟踪的球面单纯形-径向容积卡尔曼滤波算法(Spherical Simplex Radial Cubature Kalman Filter, SSRCKF),有效提高了地基雷达对再入段弹道目标的实时跟踪精度.首先,在测站坐标系下建立了再入弹道目标的非线性动力学方程和量测方程,利用四阶龙格-库塔方法得到适用于滤波计算的离散形式,具有比传统欧拉法更高的离散精度;然后,利用Spherical Simplex-Radial准则逼近非线性函数的高斯加权积分,基于贝叶斯滤波框架得到SSRCKF算法,具有比CKF更高非线性滤波精度.对再入弹道目标跟踪仿真实验中,相比于CKF算法,SSRCKF算法的定位精度提高约4.5m,定速精度提高约0.06m/s.
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