强跟踪自适应SRCKF的卫星姿态确定算法

针对卫星星敏感器/陀螺姿态确定系统在空间中存在模型不确定性、状态突变和不良测量问题,该文提出了基于强跟踪自适应平方根容积卡尔曼滤波器(STSRCKF)的卫星姿态确定算法。在平方根容积卡尔曼滤波的基础上,通过引入渐消因子,解决了由于模型不确定和状态突变引起的精度下降、稳定性差和收敛慢的问题;通过增加异常检测和引入自适应因子,获得了应对不良测量的良好跟踪能力。通过仿真实验对算法进行了验证。

Strong tracking SRCKF algorithm based-on satellite attitude determination system

When the satellite Star-sensor/Gyro attitude determination system encounter uncertain system model,breaking state and bad measurements,the filter is featured by low accuracy,poor robustness,bad stability and bad tacking ability.In this paper,basing on the strong tracking filter and squareroot cubature Kalman filter theory,a novel satellite attitude determination algorithm-Strong Tracking Adaptive Square Root Cubature Kalman filter (STSRCKF)was proposed.To get a better adaptability as STF's performance in handling with uncertain system model and breaking state problem,a fading factor was introduced into the algorithm.To ensure the positive definiteness and symmetry of the covariance matrix,a square-root decomposition method was adopted into the calculation of the covariance matrix,which improved the filter's accuracy and stability.To get a better adaptability in handling with bad measurements,anomaly detection process and a self-adapting factor was introduced into the algorithm.Simulation experiments shows that STSRCKF has higher accuracy,better robustness,stronger stability and tracking performance than STF and SRCKF in dealing with uncertain system model,breaking state and bad measurements.