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Pseudo-stochastic orbit modeling techniques for low-Earth orbiters

机译:低地球轨道者的伪随机轨道建模技术

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The Earth's non-spherical mass distribution and atmospheric drag cause the strongest perturbations on very low-Earth orbiting satellites (LEOs). Models of gravitational and non-gravitational accelerations are utilized in dynamic precise orbit determination (POD) with GPS data, but it is also possible to derive LEO positions based on GPS precise point positioning without dynamical information. We use the reduced-dynamic technique for LEO POD, which combines the geometric strength of the GPS observations with the force models, and investigate the performance of different pseudo-stochastic orbit parametrizations, such as instantaneous velocity changes (pulses), piecewise constant accelerations, and continuous piecewise linear accelerations. The estimation of such empirical orbit parameters in a standard least-squares adjustment process of GPS observations, together with other relevant parameters, strives for the highest precision in the computation of LEO trajectories. We used the procedures for the CHAMP satellite and found that the orbits may be validated by means of independent SLR measurements at the level of 3.2 cm RMS. Validations with independent accelerometer data revealed correlations at the level of 95% in the along-track direction. As expected, the empirical parameters compensate to a certain extent for deficiencies in the dynamic models. We analyzed the capability of pseudo-stochastic parameters for deriving information about the mismodeled part of the force field and found evidence that the resulting orbits may be used to recover force field parameters, if the number of pseudo-stochastic parameters is large enough. Results based on simulations showed a significantly better performance of acceleration-based orbits for gravity field recovery than for pulse-based orbits, with a quality comparable to a direct estimation if unconstrained accelerations are set up every 30 s.
机译:地球的非球形质量分布和大气阻力会在极低地球轨道卫星(LEO)上引起最强烈的扰动。利用GPS数据在动态精确轨道确定(POD)中使用了重力加速度和非重力加速度模型,但是也可以在没有动态信息的情况下基于GPS精确点定位来得出LEO位置。我们对LEO POD使用降动力技术,将GPS观测值的几何强度与力模型相结合,并研究了不同的伪随机轨道参数化的性能,例如瞬时速度变化(脉冲),分段恒定加速度,和连续的分段线性加速度。在GPS观测的标准最小二乘平差过程中,对此类经验轨道参数的估计以及其他相关参数,在LEO轨迹的计算中力求达到最高的精度。我们使用了CHAMP卫星的程序,发现可以通过在3.2 cm RMS级别上的独立SLR测量来验证轨道。使用独立的加速度计数据进行的验证表明,沿航迹方向的相关性为95%。正如预期的那样,经验参数在一定程度上弥补了动态模型中的不足。我们分析了伪随机参数的能力,以推导有关力场失模型部分的信息,并发现了证据,如果伪随机参数的数量足够大,则可以使用所得轨道来恢复力场参数。基于仿真的结果表明,重力场恢复的基于加速度的轨道的性能比基于脉冲的轨道要好得多,如果每30 s设置无约束的加速度,其质量可以与直接估计相媲美。

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