AI-Powered Space Situational Awareness (SSA) and Orbital Defense: An Intelligent System for Predicting and Preventing Unusual Spatial Movements

With the complete-scale utilization of satellites, space debris, and unidentified items in Earth's orbit, there is an urgent need for sophisticated monitoring and mitigation techniques. Physical-based Space Situational Awareness (SSA) systems and earth-based observation methodologies are the cornerstones of conventional SSA systems, with deficiencies including slow reaction rates, computational infeasibility, and diminished accuracy under highly dynamic orbital conditions. The arrival of artificial intelligence (AI) and machine learning (ML) offers SSA a paradigm shifts with the ability to identify anomalies in real time, anticipate trajectory patterns, and automate the response to threats. The present work suggests an AI-based SSA system that identifies, predicts, and prevents abnormal spatial movement through sensor fusion, deep learning algorithms, and reinforcement learning mechanisms. The architecture persists to monitor orbital properties like rotation rate, revolution trajectories, axes tilt, angular velocity, and motion patterns to detect anomalies. Long Short-Term Memory (LSTM) networks, Kalman Filters, and Hidden Markov Models (HMMs) are used to facilitate trajectory forecasting and anomaly detection, with reinforcement learning models aiding autonomous decision- making for orbital maneuver and collision avoidance. Apart from that, the proposed framework in this paper includes cybersecurity components for mitigation of anticipated cyber threats to the satellite communication networks. Simulation experiments prove 98% anomaly detection rate, 65% decrease in collision probability, and 30% increase in computational efficiency in comparison to traditional SSA methods. The results of this study authenticate the feasibility of SSA systems based on AI as a revolutionary step towards space traffic management with improved space safety and sustainability. These improved features show the better adaptability and efficiency of the proposed AI-based SSA model over the traditional physics-based tracking systems, with less than 85% anomaly detection accuracies. This leads to the practical feasibility of deploying AI for future orbital monitoring.