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Gauss meets Newton again: How to make Gauss orbit determination from two position vectors more efficient and robust with Newton-Raphson iterations

机译:高斯再次遇见牛顿:如何通过牛顿-拉夫森迭代使两个位置向量的高斯轨道确定更加有效和健壮

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Gauss's method for determining the six classical idealized Keplerian two-body orbital parameters from two position vectors and two times is elegantly formulated. It first reduces the three-dimensional velocity determination problem to two equation in two variables x, y. These two equations take the form: (i) x = F(y; l, m), (ii) y = G(x; l). Here l and m are parameters that are functions of the two position vectors and the corresponding time span between them. Gauss's approach was to form a functional iteration (y-iteration) by substituting (i) into (ii) An alternate second functional iteration (x-iteration) is formed by substituting (ii) into (i). In either case a further reduction of dimension from two to one is obtained. The method first appeared in the early-19th century and is ubiquitous in the astronomy and astro-dynamics literature. However, it could not exploited until the mid 20th century advent of powerful radars. These can provide initial elliptical orbital positional observations of such natural space objects as the moon, comets, asteroids, and meteors as well as man-made objects such as satellites and ballistic missile in the mid-course phase of their trajectories. There are, however, three basic problems with the classical approach: (1) it breaks down when the angular difference in true anomaly between the two position vectors grows larger than pi/4, (2) the classical first guess, y(0) = 1, may not lie in the convergence region of the method's functional iteration, and (3) the functional iteration, when it works, is slowly linearly convergent. In this paper we show that applying a Newton-Raphson iteration to the fixed-point equation that defines the functional iteration, in y when the angular spread is sufficiently small, or in x when the spread is large, together with trivial first guesses solves all three problems at once. Moreover, we prove a theorem on a simple necessary and sufficient condition for the existence of unique solutions and put forth a conjecture on the concavity of the two fixed-point equation graphs. This Newton-Raphson iteration approach extends the range of algorithm applicability to a true anomaly angular spread close to it, a basic singularity of the Gauss method. (c) 2007 Elsevier Inc. All rights reserved.
机译:优雅地提出了从两个位置向量两次确定六个经典理想化的开普勒两体轨道参数的高斯方法。它首先将三维速度确定问题简化为两个变量x,y中的两个方程。这两个方程采用以下形式:(i)x = F(y; l,m),(ii)y = G(x; l)。在这里,l和m是两个位置向量及其之间相应时间跨度的函数。高斯的方法是通过将(i)代入(ii)来形成功能迭代(y迭代),通过将(ii)代入(i)来形成备用的第二功能迭代(x迭代)。无论哪种情况,都可以将尺寸从2进一步减小到1。该方法最早出现于19世纪初,在天文学和天体动力学文献中普遍存在。但是,直到20世纪中叶出现强大的雷达后,它才能被利用。这些可以提供对月球,彗星,小行星和流星等自然空间物体以及人造物体(如卫星和弹道导弹)在其运动轨迹的初始椭圆轨道位置的观测。但是,经典方法存在三个基本问题:(1)当两个位置向量之间的真实异常中的角度差变得大于pi / 4时,它就会崩溃;(2)经典第一猜测y(0) = 1,可能不位于方法的功能迭代的收敛区域,并且(3)当功能迭代起作用时,它缓慢地线性收敛。在本文中,我们证明了将牛顿-拉夫逊迭代应用于定义函数迭代的定点方程,当角度扩展足够小时在y中使用,当扩展较大时在x中使用x进行平凡的初次猜测可以解决所有问题。同时出现三个问题。此外,我们证明了存在唯一解的简单而必要的充分条件,并且对两个定点方程图的凹性提出了一个猜想。这种Newton-Raphson迭代方法将算法的适用范围扩展到接近它的真实异常角度扩展,这是高斯方法的基本奇点。 (c)2007 Elsevier Inc.保留所有权利。

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