A PCA-SVM Regression Model for LEO Space Debris Orbit Prediction in an Optical Space-Based Space Surveillance Network

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.