Artificial Intelligence for Space Missions

As a trusted space partner to the U.S. government, Booz Allen helps clients modernize capabilities for today’s greatest space challenges: 

  • Weaponization of space: The U.S. must stay ahead of adversaries threatening space systems critical to our nation’s security, commerce, and way of life.
  • Addressing climate change: Space-based earth observation is essential to research.
  • Congestion: Tracking and protecting space assets is critical as satellite constellations grow exponentially.

Maneuvering Satellites in High-Stakes Situations

More than 20,000 objects, from satellites to space debris, currently orbit the earth, and a flurry of new commercial satellite releases could triple this traffic in just a few years. The space domain’s growing congestion underscores the extraordinary complexity operators face when seeking to control and protect satellites.

Booz Allen has designed and deployed multiple AI models that utilize open-source data to track the multitude of objects in Earth’s orbit, including models that detect satellite maneuvers and that can classify space object propulsion. According to Saurin Shah, a national security-focused AI business leader at Booz Allen, “Our contribution is to help clients think about how to create the platform to allow this kind of orchestration across models to drive decision making.”

Similarly, an operator may have just minutes to react to an anti-satellite missile launched by an adversary during a conflict. Although human teams might struggle to determine how to avoid the missile in time while safeguarding the satellite from objects around it, AI systems can provide rapid support. Accelerating the process many times over, trained algorithms can assess trajectory data, analyze potential actions in light of downstream effects, and recommend maneuvers and countermeasures—all fast enough so that the operator and commander can effectively respond.

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Automating Object Detection with Computer Vision

Machines now reliably complete an array of object-detection and -recognition tasks on the ground. Trained systems can discern target objects, such as people or buildings, in digital photos and videos. This AI-enabled computer vision is what lets self-driving cars spot pedestrians and other vehicles. It can also provide a “task-shedding” assist to human workers in the space realm.

In the austere environment of space, satellite systems orbit the earth, collect data, and relay that data to other satellites and to ground stations. Analysts scrutinize satellite images to find objects of interest—and custom algorithms can now complete this task for them. Like its human counterpart, the AI agent scans visuals for evidence of missile launchers or other pre-selected targets. It then applies color-coded boxes to frame the objects it detects, complementing the work of the human analyst, who is now free to focus on higher-value actions.

With the next AI advances, computer-vision systems will detect, count, and classify objects from the vantage point of space. Eventually, machines will also uncover behaviors and activities distantly occurring on Earth’s surface. At Booz Allen, we recognize the promise of activity-based intelligence (ABI) systems that move beyond simply recognizing what people and objects are doing. Onboard algorithms will one day infer and predict what’s likely to happen next, or even take the initiative to find more data to help with predictions—all without ever leaving orbit. “The process might happen in a fully automated way,” explains Patrick Biltgen, a senior AI and mission engineering expert at Booz Allen. “The system knows what missing piece of information is needed, and it goes and gets it. That would be a breakthrough in intelligence.”

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Integrating AI Into the Analytical Pipeline

Specialized analysts in intelligence centers use satellite-generated data for more than just object detection as they search for new patterns and threats. Getting the most value from all of the available intelligence means harnessing efficient, effective, and innovative analytics—and AI methods are often key to achieving this. 

Machines help agencies more efficiently collect data with autonomous sensors, sift through massive amounts of intelligence very quickly, and execute anomaly identification tasks that, in some cases, would otherwise be virtually impossible. With the support of Booz Allen-built AI systems, our clients’ human analysts can focus on interpreting threat data and making decisions about operations, counterintelligence actions, and warfighting tactics. Our leading Machine Learning Operations (MLOps) capabilities mean that organizations can now scale and standardize their AI much faster to accomplish mission objectives.

The space environment is fraught with challenges, both inherent (such as limited bandwidth and transmission opportunities) and adversarial (such as jamming and denial). In the future, Booz Allen and our clients will work toward empowering space-based platforms with AI to help machines make accurate decisions in the absence of instructions from the ground. This capability will make defense systems in space and their associated intelligence networks more resilient in the event of adversarial attack or natural disturbance.

MLOps and AIOps for Streamlined Intelligence Processes

One example of MLOps in action is with a toolset that makes capabilities like Amazon SageMaker available to those in the intelligence community. SageMaker is a cloud-based tool for building, training, and deploying machine learning models for a myriad of use cases. Because it’s cloud-based, this powerful capability was once inaccessible to intelligence community users. Through Booz Allen’s support, government data scientists can now use SageMaker via a secure portal to collaborate, reuse, and tailor machine learning models to their specific mission’s use case. When deployed within a DevSecOps ecosystem, this powerful capability provides plug-and-play machine learning models supported by all data available within the platform.

AI Operations (AIOps) is an abstract-through-concrete engineering process to operationalize AI combining responsible AI development, data, and algorithms. Our AIOps framework integrates industry practices such as DataOps, MLOps, and DevSecOps. Key tenants to our approach are reuse of common components, standardization, and an evolutionary, open architecture that can adjust to meet dynamic client requirements. These tenants enable us to develop and scale AI solutions at the speed of mission need.

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Why Space Missions Need Ethical, Transparent, and Trustworthy AI

From the outer reaches of the universe to the surface of the earth, machine learning tools can unveil new insights, simplify complex tasks, and support more effective decision making to address critical space domain challenges. But these benefits will remain incomplete unless organizations commit to using AI in fully ethical ways.

As the global focus on space increases, Booz Allen is helping U.S. organizations find ways to apply AI to the mission while ensuring ethical controls that protect human beings from bias, error, and security breaches. Using AI responsibly means putting people at the center and using machine learning to enhance, not replace, space operators’ actions. And it means infusing AI solutions with transparency and accountability guardrails that help organizations align with the values codified by the Department of Defense (DOD) and the intelligence community.

Booz Allen commits to building ethics into every stage of the AI life cycle, from design through implementation and operation. With our responsible approach to AI, we stand ready to help organizations build machine learning systems they can trust to perform critical tasks and support their human operators, analysts, and warfighters in the demanding world of space domain operations.

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