In orbital mechanics, Gauss's method for orbit determination (OD) is a popular, minimal assumption solution for obtaining the initial state estimate of a passing resident space object (RSO). Since much of the cislunar domain relies on three-body dynamics, a key assumption of Gauss's method is rendered incompatible, creating a need for a new, minimal assumption method for initial orbit determination (IOD). In this work, we present a framework for short and long term probabilistic target tracking in cislunar space which produces an initial state estimate with as few assumptions as possible. Specifically, we propose an IOD method involving the kinematic fitting of several series of noisy, consecutive ground-based observations. Once a probabilistic initial state estimate in the form of a particle cloud is formed, we apply the powerful Particle Gaussian Mixture (PGM) Filter to reduce the uncertainty of our state estimate over time. This combined IOD/OD framework is demonstrated for several classes of trajectories in cislunar space and compared to better-known filtering frameworks.

翻译：在轨道力学中，高斯轨道确定方法是一种流行且假设最少的解决方案，用于获取过境空间物体的初始状态估计。由于近地空间领域很大程度上依赖于三体动力学，高斯方法的一个关键假设变得不适用，因此需要一种新的、假设最少的初始轨道确定方法。在本研究中，我们提出了一个用于近地空间短期和长期概率目标跟踪的框架，该框架以尽可能少的假设生成初始状态估计。具体而言，我们提出了一种初始轨道确定方法，涉及对多组连续的地基噪声观测进行运动学拟合。一旦形成以粒子云形式表示的概率初始状态估计，我们应用强大的粒子高斯混合滤波器来随时间降低状态估计的不确定性。该组合的初始轨道确定/轨道确定框架在近地空间的多种轨迹类型上进行了演示，并与更知名的滤波框架进行了比较。