Monitoring this increasingly complex environment is the role of Space Situational Awareness (SSA). However, as the number of space objects and observations continues to grow, so too does the volume of data that must be processed. Utilising artificial intelligence for SSA data processing is possible, but the challenge lies in extracting meaningful information quickly enough from such data to support operational decision-making. Long before the recent growth of artificial intelligence, analysts relied on orbital mechanics, statistical estimation, and filtering algorithms to determine the position and trajectory of Resident Space Objects from radar and optical observations. Together, these technologies have the potential to enhance, rather than replace, the established analytical methods that underpin Space Situational Awareness.