为了加强对空间目标，尤其是非合作目标的探测监视，空间目标的各种特性正受到越来越多的关注。通过无迹卡尔曼滤波对空间目标的位置、速度、姿态、角速度和材料复折射率5种状态参数进行了反演估计。利用观测角度、光度和偏振度数据作为观测值估计目标的状态，基于位置与速度运动学模型和姿态与角速度动态模型，完成5种状态的时间演化，实现了对5种状态参数的联合估计。仿真结果表明，设置合理的状态初始值、状态方程和测量方程噪声，5种状态参数误差均能合理收敛，无迹卡尔曼滤波能够较好地预测空间目标的5种特征参数，同时也验证了观测角度、光度和偏振度数据可以用于反演空间运动目标的5种非直接观测特征信息。

In order to strengthen the detection and monitoring of space targets, especially non-cooperative targets, more and more attention has been paid to the characteristics of space targets. The position, velocity, attitude, angular velocity and material complex refractive index of space target were inversely estimated by unscented Kalman filter (UKF). The observation angle, photometric and degree of polarization data were used as the observation values to estimate the state of the target. Based on the position and velocity kinematics model and the attitude and angular velocity dynamic model, the time evolution of the five states was completed, and the joint estimation of the five state parameters was realized. The simulation results show that setting reasonable initial state value, state equation and measurement equation noise, the errors of five state parameters can converge reasonably, and UKF can better predict the five characteristic parameters of space targets. At the same time, it is also verified that the observation angle, luminosity and polarization data can be used to retrieve the five indirect observation characteristic information of space moving targets.