Multi-fidelity approaches to orbit-state probability density prediction reduce computation time, but introduce a systematic error in the single-point prediction of a spacecraft state. An estimate of the systematic error may be quantified using cross-validation. Credibilistic filters based on Outer Probability Measures (OPMs) enable a principled and unified representation of random and systematic errors in object tracking. The quantified error of the multi-fidelity approach defines an OPM-based transition kernel, which is used in a credibilistic filter to account for the systematic error in the orbit determination process. An approach based on automatic domain splitting is proposed to reduce the error beyond what is normally achievable with multi-fidelity methods. A proof-of-concept for the approach is demonstrated for a simulated scenario tracking a newly-detected space object in low-Earth orbit via two ground stations generating radar measurements. An OPM-based definition of the admissible region combined with the multi-fidelity credibilistic filter establishes custody of the object.

轨道状态概率密度预测的多保真方法减少了计算时间，但在航天器状态的单点预测中引入了系统误差。可以使用交叉验证来量化系统误差的估计。基于外概率测度 (OPM) 的可信过滤器可实现目标跟踪中随机和系统误差的原则性和统一表示。多保真度方法的量化误差定义了基于 OPM 的过渡内核，该内核用于可信过滤器中，以解释定轨过程中的系统误差。提出了一种基于自动域拆分的方法，以将错误减少到超出多保真方法通常可实现的范围。该方法的概念验证通过两个生成雷达测量值的地面站在低地球轨道上跟踪新探测到的空间物体的模拟场景进行了演示。基于 OPM 的可接受区域定义与多保真可信过滤器相结合，建立了对象的保管权。