🤖 AI Summary
This work proposes a unified space situational awareness framework to address the challenges posed by the highly nonlinear, non-Keplerian dynamics in cislunar space, which degrade state estimation accuracy, alongside complexities in remote sensing, sensor placement, and task scheduling. The framework uniquely integrates cost-optimized observer architecture design, mutual information–driven task scheduling, and high-frequency orbit-attitude joint estimation via an error-state multiplicative unscented Kalman filter. Sensor configurations are optimized using the Tree of Parzen Estimators, substantially reducing system cost. Simulations demonstrate that the proposed approach maintains robust orbit estimation performance while significantly decreasing the number of required sensors, further revealing scalable trade-offs among resource allocation, scheduling strategy, and overall system performance.
📝 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.