中文摘要：

针对空间目标无源定位问题。建立了空间目标的系统状态模型和天基仅测角观测模型．设计了卫星观测空间目标无源定位系统。系统具有强非线性和高维度的特点。传统扩展卡尔曼滤波（EKF）算法线性化过程复杂、舍入误差大。针对上述问题，提出了贝叶斯框架下的平方根容积卡尔曼滤波（SCKF）算法，采用后验概率密度函数（PDF）对空间目标进行估计定位。仿真结果表明，SCKF算法的收敛精度优于传统的扩展卡尔曼滤波算法和无迹卡尔曼滤波算法（UKF），证明了SCKF算法在空间目标无源定位中的有效性和应用价值。

英文摘要：

To research the tracking of the maneuvering spatial target, the spatial target motion mode｜ and maneu- vering model applying bearing-only measurements are developed, and the spatial target passive localization system is designed. Due to the strong nonlinearity of spatial targets, the linearization in traditional Extended Kalman Filter （EKF） is complicated, and there is big rounding error at the same time. To solve this problem, the posterior proba- bility density function （PDF） is used, and Square-root Cubature Kalman Filter （SCKF） in Bayesian framework is applied to spatial target passive location. The result of simulation shows that the accuracy of SCKF is better than tradi- tional EKF and UKF. The results also show the effectiveness and the practical valuableness of SCKF in the spatial tar- get passive localization system.