1. Introduction The exponential growth of space missions has led to an alarming increase in space debris, creating significant risks to operational satellites and space missions. Space debris comprises defunct satellites, spent rocket stages, and fragments resulting from collisions and disintegration. This project leverages IBM Watsonx AI capabilities to design a system for space debris tracking and collision prediction, aiming to provide actionable insights to mitigate risks and ensure sustainable utilization of space.

3. Methodology 3.1 Data Collection The system will incorporate multiple datasets for analysis and prediction: 1. Space Debris Data ○ Global Space Debris Monitoring and Removal Market Report: Contains market trends, technologies, and operational insights. 2. Near Earth Objects (NEOs) Dataset ○ NASA Nearest Earth Objects Dataset: Provides data on NEOs, their size, velocity, and proximity to Earth. 3. Additional Sources ○ Search for publicly available datasets containing satellite trajectories, debris distribution, and orbital parameters from organizations like ESA, NASA, and commercial entities. 3.2 Data Processing ● Preprocessing: Clean and normalize data to remove inconsistencies and standardize formats. ● Integration: Combine multiple datasets to create a comprehensive view of debris dynamics. ● Feature Engineering: Extract critical features such as orbital velocity, inclination, size, and proximity. 3.3 Predictive Modeling ● Utilize IBM Watsonx AI to develop a machine learning model capable of: ○ Tracking debris movements in real-time. ○ Predicting the likelihood of collisions based on orbital paths and debris density. ○ Classifying debris risk levels based on size and proximity to critical assets. 3.4 RAG Implementation ● Use IBM Watsonx to implement Retrieval-Augmented Generation (RAG) on PDF documents containing country-specific policies and technical reports on space debris. ● Extract relevant information about regulations, mitigation strategies, and technological developments. ● Present this data in a user-friendly dashboard, accessible to stakeholders for informed decision-making. 3.5 Visualization and Alert System ● Design an interactive dashboard powered by IBM Watsonx: ○ Display real-time debris tracking using orbital data. ○ Provide predictive collision alerts with suggested mitigation actions. ○ Offer insights based on country-specific policies and global trends.

6. Future Work ● Integrate advanced satellite imagery analysis for more precise debris tracking. ● Expand collaboration with global organizations for data sharing and standardization. ● Develop autonomous collision avoidance systems for operational satellites using AI-driven predictions. 7. Conclusion This project aligns with IBM’s commitment to sustainability and responsible AI by addressing a critical challenge in the modern space era. By leveraging IBM Watsonx’s capabilities, this initiative provides a scalable and actionable solution to mitigate space debris risks, ensuring the long-term sustainability of space exploration and utilization