The primary objective of this study is to investigate the efficacy of Support Vector Machine (SVM) regression method to enhance the accuracy of Low Earth Orbit (LEO) space debris orbit prediction using the historical data. Principal Component Analysis (PCA) is employed to efficiently reduce the dimensionality of dataset’s feature space and hence optimize the model’s performance. This investigation is motivated by the limitations of conventional orbit prediction methods, which often rely on dynamic models with unknown coefficients of perturbation forces and other relevant characteristics of space debris, leading to errors during the prediction process. On the other hand, while the Collision Avoidance Maneuver (CAM) strategy remains crucial for mitigating the threat posed by such debris, precise knowledge of debris coordinates is essential for effective CAM implementation. However, traditional ground-based optical equipment encounters challenges in observing fast-moving debris within the dynamic LEO environment, including atmospheric interference and limited Field of View (FOV). To address these limitations, the secondary objective of this study involves exploring the potential of an in-orbit optical space surveillance network as a promising solution. The system utilizes optical sensors distributed across multiple spacecraft within the Above the Horizon (ATH) constellation, specifically designed to continuously monitor the most densely populated altitude band in LEO. Simulations under different conditions demonstrate that the proposed scheme successfully complements ground-based equipment and dynamic models for debris tracking, thereby improving orbit prediction accuracy. The results of simulations under different conditions demonstrate that proposed scheme successfully complements ground-based equipment and dynamic models for debris tracking, and improving orbit prediction accuracy.

中文翻译：

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光天基空间监视网络中 LEO 空间碎片轨道预测的 PCA-SVM 回归模型

本研究的主要目的是研究支持向量机（SVM）回归方法的有效性，以提高使用历史数据进行低地球轨道（LEO）空间碎片轨道预测的准确性。采用主成分分析（PCA）来有效降低数据集特征空间的维数，从而优化模型的性能。这项研究的动机是传统轨道预测方法的局限性，这些方法通常依赖于扰动力系数和空间碎片其他相关特征未知的动态模型，导致预测过程中出现错误。另一方面，虽然避碰机动 (CAM) 策略对于减轻此类碎片造成的威胁仍然至关重要，但精确了解碎片坐标对于有效实施 CAM 至关重要。然而，传统的地面光学设备在观测动态低地轨道环境中快速移动的碎片时遇到了挑战，包括大气干扰和有限的视场（FOV）。为了解决这些限制，本研究的次要目标是探索在轨光学空间监视网络作为一种有前途的解决方案的潜力。该系统利用分布在地平线以上（ATH）星座内多个航天器的光学传感器，专门设计用于连续监测低地轨道上人口最密集的高度带。不同条件下的仿真表明，该方案成功补充了碎片跟踪的地面设备和动态模型，从而提高了轨道预测精度。不同条件下的仿真结果表明，所提出的方案成功地补充了用于碎片跟踪的地面设备和动态模型，并提高了轨道预测精度。