融合深度神经网络与约束容许域的地月空间航迹智能分类

Objective The growing number of lunar and deep-space exploration missions has transformed the Earth-Moon environment into a dynamic and complex orbital domain, where the ability to rapidly and accurately classify trajectory families is critical for mission planning, space situational awareness, and orbital debris management. Traditional approaches, such as statistical clustering of orbital elements or permissible domain methods dependent on manually established boundaries, face critical limitations in this context. These methods fail to adequately address the nonlinear dynamics and analytical intractability of three-body gravitational interactions, resulting in low classification accuracy and computationally intensive orbit determination processes. To address these challenges, this study proposes a novel hybrid framework called convolutional neural network-constrained admissible region (CNN-CAR), which combines data-driven feature learning with physics-based constraints. By synergizing the pattern recognition capabilities of CNN with the dynamical principles of the circular restricted three-body problem(CR3BP), the framework aims to improve the computational efficiency of orbit determination and the precision of orbital family attribution, particularly for trajectories of unknown origin. This advancement provides a robust basis for real-time cataloging, autonomous trajectory screening, and reliable identification of objects in cislunar space. Method The CNN-CAR framework is developed through a structured three-stage process. Initially, a comprehensive database of known Earth-Moon trajectory families is constructed, encompassing halo orbits at L1 and L2, as well as distant retrograde orbits(DROs). This database is populated with high-fidelity orbital parameters obtained from the JPL Horizons system. A CNN is subsequently trained on this dataset to automatically extract high-dimensional dynamical features from orbital state vectors, including positional coordinates and velocity components. The trained CNN functions as a feature extraction engine, capable of discerning complex nonlinear relationships among these parameters that are often obscured in traditional methods. In the second stage, initial CARs are defined using Jacobi constant constraints based on the CR3BP. These constraints establish permissible parameter ranges that reflect the dynamical stability criteria of each trajectory family. The CNN’s classification outputs are then integrated with these physics-informed boundaries to dynamically refine the CARs. This integration systematically excludes regions that are outside the learned feature clusters and inconsistent with the Jacobi thresholds, thereby narrowing the search space for orbit determination tasks while preserving physical validity. In the final stage, unknown trajectories are classified through a fusion mechanism that leverages both the deep semantic features extracted by the CNN and the predefined CARs for distinct orbital families. The framework evaluates the compatibility of a trajectory’s inferred Jacobi constant with the CARs and its similarity to the learned feature patterns. This approach enables the autonomous assignment of trajectories to the most probable orbital family even under conditions of uncertainty. Result The performance of the proposed framework was rigorously validated through numerical simulations in a representative scenario where the observational platform was positioned on a DRO. Three primary orbital families-HaloL1, HaloL2, and DRO-were selected for comprehensive testing of classification accuracy and CAR optimization. Compared with conventional methods that rely solely on Jacobi constant constraints, CNN-CAR achieved a significant reduction in the admissible domain area, exceeding 50% as demonstrated in the simulations. This reduction not only minimizes the computational effort required for orbit determination but also maintains consistency with the constraints derived from the CR3BP. In the classification of unknown trajectories, the hybrid CNN-CAR model achieved a remarkable success rate of 50%, nearly doubling the accuracy of the standalone CAR method(25%). This substantial improvement is attributed to the CNN’s ability to discern subtle nonlinear features that are unattainable through purely physics-based methods. The framework also exhibited remarkable robustness against observational noise, maintaining a stable recognition rate of 50% even when positional uncertainties reached 1 000 arcseconds. Conclusion The proposed CNN-CAR hybrid framework represents a step in trajectory classification and orbit determination within the Earth-Moon system. By unifying machine learning’s adaptability with the rigor of classical astrodynamics, the method overcomes the drawbacks of traditional techniques, balancing computational efficiency and physical interpretability. The dynamic optimization of admissible regions, guided by learned features and Jacobi constant constraints, effectively reduces search spaces while maintaining consistency with three-body dynamics. This ability is essential for real-time operations in complex orbital environments. The framework’s enhanced accuracy for unknown trajectories advances autonomous space situational awareness, enabling rapid identification of uncharacterized objects such as space debris or non-cooperative spacecraft. In addition, the framework shows robust performance under high-noise conditions and varied observational scenarios, suggesting its potential for practical applications, such as lunar orbital servicing, cislunar debris mitigation, and multi-objective trajectory planning. This work highlights the benefits of combining data-driven methods with physics-based constraints. It provides a reliable approach to addressing orbital management challenges in the Earth-Moon system and offers a foundation for intelligent trajectory analysis in complex gravitational systems.