Linking Large Language Models for Space Domain Awareness

SPEED READ

• There’s an urgent need for advanced AI as an expected surge in the number of active satellites by 2030 makes space increasingly “competitive, congested, and contested.” Networking large language models (LLMs) can enhance space domain awareness and address these challenges. 
• The BRAVO hackathon showcased the transformative capabilities of networked LLMs in space operations. By linking these models, they can communicate, share data, and amplify space operators’ awareness, leading to more efficient and precise decision making. 
• By allowing LLMs to collaborate and learn from each other, networking them can provide comprehensive insights into space behaviors and threats. This interconnected system promises enhanced space domain awareness and strategic advantage. 

Crowded orbits, fast-moving adversaries, the number of active satellites expected to grow to more than 60,000 by 2030—space leaders have a challenge only advanced AI can address. Space domain awareness, the practice of tracking and understanding factors that can affect U.S. space operations, requires integration and calculations beyond current capabilities. We find that a new approach, networking large language models (LLMs), can accelerate capabilities across an enterprise of global partners. 

The U.S. Needs to Move Faster in the New Space Race 

Once envisioned as a sanctuary for exploration and other peaceful pursuits, space is now increasingly “competitive, congested and contested.” Rival nations are developing anti-satellite tactics and weapons, militarizing the domain even as a new commercial space industry is booming—centered on initiatives from selling space data to building rockets to further NASA’s mission to Mars. 

Moreover, proliferated satellite constellations are being rapidly developed and launched in clusters. The first mega-constellation was launched in 2019; four years later, these groups already make up more than half of active satellites. A single network can contain hundreds or even thousands of satellites—most notably Starlink, which plans to expand its fleet to as many as 42,000. This significantly crowds a domain that has become critical for daily living. From national security to climate science, communications to traffic directions, we depend on satellite services to be there when we need them. 

All these dependencies create an urgency to adopt innovations and strategies that will ensure the U.S. and its allies stay at the forefront of space—and knowing the location of space assets and their operators’ intent is foundational to that goal. 

Launching an Era of Higher Risk 

As space capabilities continue to accelerate, risk will rise along with reward. 

• Expected satellite growth: The total number could top 60,000 by 2030. 
• Estimated space junk: More than 100 trillion pieces may already be orbiting. 
• Expanded risk: Even tiny paint flecks can damage a satellite. 

Celestial Chess: A Life-or-Death Game 

Staggering as it is to contemplate a trillion objects traveling through space, the challenge isn’t just about the computational power required. It’s about transforming a process where operators manually track data on multiple screens into a system capable of integrating complex datasets, automating processes, and applying advanced algorithms to add a new level of 

precision—enabling predictive analytics and, ultimately, recommended courses of action. 

The complexities can be compared to a game of chess—a game computers have become famously good at—played in multiple dimensions, with decisions made at split-second speed. Some of the factors: 

• Objects need to be tracked in multiple orbits. Most commercial satellites, human space missions, and the International Space Station are in low Earth orbit (LEO), a regime that extends to about 2,000 km. Higher orbits, such as medium Earth orbit (MEO) and geostationary orbit (GEO), host navigation, weather, communications, and national security satellites. NASA’s Artemis program and robotic adjuncts from multiple nations generate more traffic between Earth and the Moon. Operating safely in this region of cislunar space requires new technologies and tactics for object detection, forecasting, and collision avoidance. 

• Data pours in from multiple sensors from multiple sources. This results in a profusion of siloed datasets in multiple formats that need to be ingested and processed, with granular security applied. Although the increased number and diversity of data sources improve our ability to perform space domain awareness, it also introduces a data fusion challenge as multiple data sources with different formats and reference frames must be integrated in real time. 

• Some of the most critical datasets are classified, requiring laborious manual processes to share across domains. Although technical cross-domain solutions exist, they often can’t keep pace with new file types and data structures and lack the resiliency to keep operating under stress or adversarial attack. 

• Modeling threat scenarios requires a vast amount of data to train algorithms. As space defense is a relatively new area, data is scarce and, in some cases, doesn’t yet exist. Therefore, generating synthetic data is a necessary step, with attendant responsibilities, such as ensuring algorithms are free of bias. 

Tracking the Future: The BRAVO Hackathon 

LLMs—deep learning algorithms that generate content and perform other complex functions using very large datasets—exploded in popularity following the launch of OpenAI’s ChatGPT in the fall of 2022. While most users have been experimenting with creating poetry, writing essays, or paraphrasing information, Booz Allen has been exploring new applications for LLMs across space- domain applications. 

The capability to network LLMs was demonstrated in spring 2023 at the Air Force’s BRAVO hackathon, a multi-classification event drawing over 400 experts to compete in prototyping data solutions for pressing problems. The award for best data visualization and the Best User Interface award went to a team that linked two LLMs in a classified environment using zero trust protocols. 

The hackathon gave the team a chance to give ensemble modeling—a process for improving multiple diverse algorithms to arrive at an outcome—new power by networking LLMs rather than individual algorithms. This opened a new path to generate the fast, comprehensive answers required to move space operations with speed and accuracy. It also provided two-way communication between specialized LLMs to amplify space operators’ awareness. 

After rapidly deploying a user interface, the team deployed two LLMs and wrote an app that allowed them to talk with each other (see Figure 1). The first LLM was trained in radar sensor data, while the other was trained in Earth observation (EO) imagery. 

The team executed a scenario where the team member, acting as operator, asked the first LLM to watch a certain area in Asia and send an alert if anything of interest was found. No special codes were needed; the operator simply typed the request as if texting a colleague. In practice the request could have been activated another way, according to operator preference; for example, via voice recognition. 

The first LLM, designated as moderator, located a radar image and asked the second model if it had any data. The second LLM, trained in EO, responded that it did and sent the image along. The first LLM then delivered both images to the operator along with a message saying, essentially, “I found a radar image at that location and retrieved an EO image at that same location.” The process was simple and streamlined for the human partner. 

“Software building conferences like BRAVO allowed us to push, and sometimes stumble, on some interesting solutions,” said Collin Paran, the AI solutions architect who led the Booz Allen team. “Linked, multimodal, and networked AI with two-way communication will certainly unlock more insights for different organizations.” 

Why Networking LLMs Dramatically Increases Space Awareness 

The synergy of LLMs working together and delivering ever more detailed, insightful results makes this approach significant. Say you have a mission-focused LLM trained on the Space Command’s catalog of objects in orbit, the Unified Data Library. Imagine you network it with an LLM trained in avoiding collisions, called conjunctions; one trained on radar data; and another trained on a military intelligence database of adversarial threats. You’ve conducted skilled training and testing, and you’ve been using the system for increasingly critical tasks. 

Now the system is deployed on a mission where the operator wants to understand the behavior of a particular set of satellites. The primary LLM identifies the satellites and, equipped with past data, knows that the last 40 times that constellation passed in that orbit in that configuration, the satellites moved slightly closer to a U.S. Space Command satellite. 

Because the LLM is also configured with the other models, it can query those LLMs on the behavior as well. As a result, the primary LLM can inform the operator that this constellation has recently been flagged for moving out of its orbit and its speed is increasing just enoughto create that devastating conjunction. And thanks to information from the LLM trained on adversarial threats, the primary LLM could also queue up details for the operator identifying tactics that could be at play. 

Going one step further, we can imagine one of the LLMs is trained in the physics of the problem. The primary LLM could use this capability to extrapolate possible scenarios and courses of action—perhaps recommending a maneuver that uses less fuel or is less disruptive to the orbits of other satellites. The human remains in charge and is empowered to make a more confident decision, faster. 

Knowledge gained on this encounter will feed into the primary LLM’s learning process. And because it is linked— nested with the other LLMs to provide hierarchical, context-based learning—it gains knowledge from them at a massive rate. Its information is continuously updated as the nested LLMs are trained and validated on the latest feeds in their vast database. Every event helps the system become steadily more intelligent, creating an ever more sentient space domain awareness capability. 

Adding Classified Data—and What It Takes to Do It 

The crowding of space requires higher accuracy in tracking and predicting space movement, requiring data from all sources—especially classified data, traditionally difficult to share. A critical aspect of the linked approach is that networking LLMs has been demonstrated in a secure enclave, using both classified and unclassified data. 

The innovation can be taken to space organizations by teams with a development environment built on open architectures, infrastructure that leverages government-owned technologies, zero trust architecture, and experience providing flexible modernization for government missions. For example, networking LLMs requires the same granular security policies as military initiatives, such as Joint All-Domain Command and Control (JADC2). 

Automated DataOps ensures the onboarding of diverse feeds, standardizing formats and enforcing data standards and policies plus providing granular security. Common tools and interoperable technologies simplify development. And cross-domain solutions ensure automated workflows with modular elements that can be adapted for mission demands. 

Amplifying AI Advantages 

Real-time communication and collaboration between LLMs that are continually trained on trusted data opens the way to multiple advances. For example, the practice: 

• Frees up operators to focus on assessment rather than switching screens and manually evaluating and comparing data to anticipate threats. 
• Enables automated fusion of classified, civil government, and commercial data to train more powerful, precise AI models. 
• Provides each stakeholder with automatic access to data, improving decision making for stakeholders across domains. 

Training, Testing—and Then Trusting 

The concept of training LLMs to be networked starts with a focus on the mission and ensuring compliance with ethical guidelines like the AI Bill of Rights and the NIST AI Risk Management Framework. AI scientists need to confirm that data sources, including other LLMs, are trained on unique datasets from verifiable sources. 

Developers can quickly incorporate intelligent agents and tools that integrate easily with trusted sources once data ingestion is assured and a training pipeline and prompt templates are built. Meanwhile, strategies can streamline the process. For example, training on servers before migrating systems to the cloud saves on costly cloud computing. 

Focused, nested training using trusted data and ensuring a strategic intersection between the LLMs is critical to ensure rapid, accurate returns. Data scientists need to go through the system and assess different weights, inputs, and other components, testing its information with truth data and then entrusting it with small tasks as a first step to more strategic ones. For example, it could be asked to develop a red-team attack scenario that the human experts can incorporate into a training exercise. 

Linking LLMs Can Launch Adaptive Space Awareness 

As General Chance Saltzman, the Space Force’s chief of space operations, emphasizes, resilience is essential and continuous awareness is critical for the Space Force’s strategy of competitive endurance. 

Networking LLMs to leverage their unique strengths ensures real-time advances as they collaborate on tasks. Linking these models is a practical way to deliver increasingly powerful results. It’s scalable, allowing the networking of multiple LLMs. It’s model-agnostic, so it can be used with any LLM. And it holds the promise of connecting the vast, siloed datasets that are key to avoiding celestial collisions and countering adversarial attacks. 

Ron Craig is vice president of space strategy and solutions at Booz Allen. 

Michelle Harper leads software projects that accelerate integrated capabilities for Booz Allen clients, including the Space Force. 

A version of this article first appeared in Velocity, a Booz Allen publication that dissects the unique issues at the center of mission and innovation.