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GPS-based onboard real-time orbit determination for leo satellites using consider Kalman filter

机译:考虑卡尔曼滤波器的基于GPS的狮子座星载实时轨道确定

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The use of Global Positioning System (GPS) observables provides the primary and advantageous technique for satellite orbit determination in low earth orbit (LEO), in particular when onboard autonomy is required. A wide range of LEO spacecraft equipped with GPS receivers have been launched into space for different applications. Aiming at determining the position and velocity of the satellite in real-time, a consider Kalman filter (CKF)-based reduced-dynamic orbit determination (RDOD) is introduced in this paper. In the CKF, orbit dynamic model is simplified to meet the space-borne computational limitations. The atmospheric drag and solar radiation pressure coefficients are considered rather than estimated in the conventional RDOD strategy. Only a lower order and degree of an earth gravity model is used in the orbit model. However, the propagation of the covariance of the consider parameters is able to absorb the unmodeled and dismodeled perturbations. Therefore, the filter could become convergent with desirable orbit determination performance. The CKF-RDOD method is implemented with a set of the Gravity Recovery and Climate Experiment flight data. The solutions indicate that this proposed method could achieve satisfactory precision orbit determination with approximately 1.5 m level of three-dimensional root-mean-square error using GPS broadcast messages in real-time scenarios.
机译:全球定位系统(GPS)观测值的使用为确定低地球轨道(LEO)中的卫星轨道提供了主要且有利的技术,特别是在需要机载自治的情况下。各种配备有GPS接收器的LEO航天器已被发射到太空,以用于不同的应用。为了实时确定卫星的位置和速度,本文介绍了一种基于卡尔曼滤波器(CKF)的降动态轨道确定(RDOD)方法。在CKF中,简化了轨道动力学模型以满足星载计算的限制。在常规RDOD策略中考虑而不是估计大气阻力和太阳辐射压力系数。在轨道模型中仅使用低阶和度数的地球重力模型。但是,考虑参数协方差的传播能够吸收未建模和未建模的扰动。因此,滤波器可以收敛于期望的轨道确定性能。 CKF-RDOD方法是通过重力恢复和气候实验飞行数据集实现的。解决方案表明,该建议方法可以在实时场景中使用GPS广播消息以约1.5 m级别的三维均方根误差实现令人满意的精度轨道确定。

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