Abstract
The cislunar regime departs from near-Earth orbital behavior through strongly non-linear, non-Keplerian dynamics, which adversely affect the accuracy of uncertainty propagation and state estimation. Additional challenges arise from long-range observation requirements, restrictive sensor-target geometry and illumination conditions, the need to monitor an expansive cislunar volume, and the large design space associated with space/ground-based sensor placement. In response to these challenges, this work introduces an advanced framework for cislunar space domain awareness (SDA) encompassing two key tasks: (1) observer architecture optimization based on a realistic cost formulation that captures key performance trade-offs, solved using the Tree of Parzen Estimators algorithm, and (2) leveraging the resulting observer architecture, a mutual information-driven sensor tasking optimization is performed at discrete tasking intervals, while orbital and attitude state estimation is carried out at a finer temporal resolution between successive tasking updates using an error-state multiplicative unscented Kalman filter. Numerical simulations demonstrate that our approach in Task 1 yields observer architectures that achieve significantly lower values of the proposed cost function than baseline random-search solutions, while using fewer sensors. Task 2 results show that translational state estimation remains satisfactory over a wide range of target-to-observer count ratios, whereas attitude estimation is significantly more sensitive to target-to-observer ratios and tasking intervals, with increased rotational-state divergence observed for high target counts and infrequent tasking updates. These results highlight important trade-offs between sensing resources, tasking cadence, and achievable state estimation performance that influence the scalability of autonomous cislunar SDA.
Abstract (translated)
地月空间态势感知体系因强非线性、非开普勒动力学而偏离近地轨道行为特性，这种动力学特性会不利影响不确定性传播与状态估计的精度。此外还面临诸多挑战：远距离观测需求严苛、传感器-目标几何构型与光照条件受限、需监控广阔的地月空间范围，以及天基/地基传感器部署方案存在巨大的设计空间。针对这些挑战，本研究提出一种先进的地月空间态势感知框架，涵盖两项核心任务：(1) 基于真实成本模型的观测器架构优化——该模型捕捉关键性能权衡关系，采用帕岑树估计算法求解；(2) 利用优化后的观测器架构，在离散调度周期执行互信息驱动的传感器调度优化，而在连续调度更新之间采用误差状态乘法无迹卡尔曼滤波器进行更高时间分辨率的轨道与姿态状态估计。数值仿真表明：任务1的方法能生成显著优于基线随机搜索方案的观测器架构，在实现更低成本函数值的同时减少传感器数量；任务2结果显示，平移状态估计在广泛的目标-观测器数量比范围内保持良好，但姿态估计对目标-观测器数量比及调度间隔更为敏感——当目标数量较多且调度更新频率较低时，旋转状态发散现象会加剧。这些结果揭示了感知资源、调度频率与可达成状态估计性能之间的关键权衡关系，对自主地月空间态势感知系统的可扩展性具有重要影响。
URL
https://arxiv.org/abs/2603.20579
