As space traffic increases and our reliance on satellites grows, the question of how to manage and predict anomalies in orbital celestial mechanics becomes increasingly important. The idea that artificial intelligence (AI) might provide a solution is appealing. However, the reality is that AI alone may not be sufficient to address the complexities and uncertainties inherent in the space environment.
Thirty years ago, limitations in computation and communications made it nearly impossible to manage and process the kinds of datasets we work with today. The explosion in data availability, combined with advanced search algorithms and AI techniques, has revolutionized our ability to draw conclusions from vast, interconnected datasets. However, in fields such as orbital celestial mechanics, the challenge is not just the quantity of data, but the quality and relevance of that data. This is especially true when it comes to rare events such as anomalies and collisions.
AI and probabilistic models excel in domains where data is abundant and patterns occur frequently. In contrast, the rarity of anomalies in satellite data combines with the dynamic and perturbative nature of orbital mechanics to make traditional AI techniques difficult to apply with confidence. Without robust, anomaly-rich datasets, AI predictions in this context are often speculative rather than definitive.
Operator experience is a key factor in anomaly detection. When data is scarce and uncertainty is high, human intuition and expertise are important. AI and human expertise must complement each other: AI can help process and identify potential anomalies, but experienced operators are needed to interpret these findings in a meaningful way.
Controlling the number of satellites and increasing observation frequency to maintain orbit estimation accuracy may be the most reasonable approach to space traffic management. As the number of satellites continues to grow, the need for more accurate and frequent tracking becomes critical to avoid collisions and maintain the long-term sustainability of space operations.
AI may not be able to identify uncooperative and dangerous orbital anomalies. Anomalies, simply put, are unusual and rare events, and more data or better algorithms may not be of much use as there may not be a sufficient number of such one-off events to train an algorithm. Addressing the unique challenges posed by the dynamic and unpredictable nature of the space environment requires a combination of AI and human expertise.
AI has great potential in many areas, but its limitations are clear when it comes to detecting anomalies in orbital celestial mechanics. Due to the lack of reliable data and the inherent uncertainty of orbital mechanics, AI cannot yet replace human judgment in this field. In the future, the integration of AI and human expertise will be key to managing the increasingly complex space environment.
David Finkleman served as Chief Technology Officer for USSPACECOM and NORAD for nearly 20 years and was Senior Scientist at the AGI Space Standards and Innovation Center for 10 years. He is a member of the International Academy of Astronautics, a Life Fellow of the AIAA, and has been elected Fellow of numerous professional organizations. He is retired from the U.S. Air Force and senior federal executive branch. He served as Director of Technology and Systems for Army Ballistic Missile Defense, Deputy Program Manager for Naval Directed Energy Weapons, and Chair of ISO Space Operations Standards. He holds a PhD from MIT and has more than 60 years of aerospace experience.
