Leveraging AI/ML To Protect The Space Domain

Space and its services no longer only impact the warfighter. With assured position, navigation, and timing, and international data communications now reliant on space assets, threats to commercial, civil, and DoD spacecraft risk disruption to our daily lives. The existing congestion of space, combined with the increasing pace of spacecraft launch, has heightened the competitive nature of space and demand for Space Domain Awareness (SDA). Our ability to monitor, manage, and control space assets is foundational to maintaining U.S. space dominance. However, awareness without context and rapid response capabilities is useless.

The growing number of space, aerial, and ground assets generating data for SDA purposes is beyond the capability of human observers to consume. Currently, information is stored in huge databases for data trending analysis and historical reference. Training operators to see potentially aggressive actions and respond with defensive maneuver plans to avoid conflict is slow, limited in scope, and unsustainable. Leveraging Artificial Intelligence and Machine Learning (AI/ML) to correlate and propagate spacecraft maneuvers provides actionable data and drives automated responses.

The application of AI/ML in SDA allows for active and continuous monitoring of all space assets without requiring hordes of data analysts. AI/ML analyzes commercial and private data feeds for correlation with civil and DoD data to predict the probability of accuracy. This provides weighted accuracy scores for identifying hostile Unidentified Anomalous Phenomena (UAPs) to be correlated with known foreign asset libraries. Given the “high enough” probability of a UAP and observed aggressive maneuvers, the AI/ML algorithm alerts operators of possible hostile action. In parallel, AI/ML produces defensive maneuver plans, schedules antenna time on the next available ground site, and engages DoD assets to perform space-based surveillance of the UAP.

Within minutes of the threat being detected, the U.S. Space Force monitors and tracks custody of the UAP and predicted threatened spacecraft. Operators maintain awareness of the situation through a virtual reality headset with a digital twin of U.S. space assets, projected orbit paths, and critical data synthesized into digestible statuses. The UAP markings, location, size, and radio frequency signature are cross-referenced with the Unified Data Library to identify it as a known hostile asset. All information, plans, and actions are compiled into a report for the next round of international negotiations and discussions.

Enabling the rapid response of the AI/ML algorithm is a Parsons classified cloud-based, scalable Ground Operations Center as a Service (GOCaaS) with humans in the loop for monitoring and approval purposes. GOCaaS uses a repeatable classified architecture to fly commercial, civil, and DoD space assets. Geographically distributed data centers host operational clusters for redundancy and load balancing purposes, while the digital twin and databases are stored in replicated file structures for quick access and recovery.

Our engineers leverage the Agile process to support a DevSecOps approach with a continuous integration/deployment pipeline. Parsons creates this reality through product and service development to preserve and protect our legacy in the stars. We’re leveraging domain knowledge to perform large system integration to develop solutions today for tomorrow’s threats.

About The Author

Curtis Bell, Lead Solution Architect for Parsons Corporation’s Space Engineering Solutions, has been in the industry for over 15 years and has experience in all aspects of space systems from developing spacecraft components to ground operations. He received his B.S. in Computer Engineering from UCCS and M.S. in Engineering Management from Penn State.

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