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A Research of Gyro/Star-Sensor Integrated Attitude Determination Based on Particle Filter

机译:基于粒子滤波的陀螺/星传感器综合姿态确定研究

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The improved particle filter is applied to satellite attitude determination of Gyro/Star-Sensor integrated system in this paper, including EKPF(Extended Kalman Particle Filter) and CKPF(Cubature Kalman Particle Filter). The improved filter uses EKF or CKF to generate sophisticated proposal distributions with the new observation in comparison to the general PF and it avoids the limitation of the EKF and the CKF which only apply to Gaussian distribution. The system models are established in which attitude quaternion and gyro bias are process parameter and outputs of star-sensors are measurement parameters. The results show that, by the suggested method, the rate of convergence is higher and the determination accuracy is improved, especially when the measurement noise is non-Gaussian.
机译:本文将改进的粒子滤波技术应用于陀螺/星传感器集成系统的卫星姿态确定中,包括EKPF(扩展卡尔曼粒子滤波)和CKPF(卡尔曼粒子滤波)。与常规PF相比,改进的滤波器使用EKF或CKF生成具有新观测值的复杂建议分布,并且避免了仅适用于高斯分布的EKF和CKF的限制。建立了以姿态四元数和陀螺偏向为过程参数,星形传感器输出为测量参数的系统模型。结果表明,所提出的方法收敛速度更高,并且提高了测定精度,特别是在测量噪声为非高斯噪声的情况下。

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