While agile constellations of small satellites capture the imagination due to their resiliency and flexibility, it’s the “brains”—the ground system—of each network that direct the mission and secure its success. Command and control (decision making), managing the mission (planning, testing, and operations), and processing and disseminating data are all dependent on the ground system and its operators.

Automating key activities will allow operators to focus on critical decisions and enable seamless integration of systems that are currently stovepiped. Modernizing these functions is critical to keep U.S. space aspirations aloft.

Though $150.4 billion was spent on ground segment technologies in 2023 according to a June 2024 press release from the Satellite Industry Association, many space organizations still have to contend with legacy equipment.

What’s more, there’s no guarantee that new ground systems are using the latest software innovations needed to make systems smart, scalable, and secure. That’s because new ground systems are often included in the contract for an overall space system, which means contractors used to building legacy systems may build the system using techniques they’re already familiar with rather than the latest software innovations. 

Imagine you’re a satellite operator working in a military or intelligence organization. Chances are, you’re working at a brick-and-mortar ground station performing actions such as maintaining satellite surveillance of a given region, tracking satellites and other space assets, or providing satellite imagery to support a military operation.

You do this by directing a satellite using instructions, or “taskings,” which include multiple actions: defining the area of interest, finding an available constellation, deciding between radar or optical images, ascertaining the viewing angle, and so on. You track different assets across several screens, which adds to the complexity of your work.

Let’s say a satellite spots an adversary’s troops moving on the ground near a contested area. That satellite isn’t scheduled to make another pass over the area for eight hours, so you decide to task another satellite for reconnaissance. It’s an inefficient and time-consuming process.

First, you need to decide whether to turn to a commercial provider or use a classified source. Then, depending on what assets are available and conditions that are beyond your control, it could take anywhere from minutes to hours to get one or more satellites in place and begin downloading images.

For example, if the weather is partially overcast in that region, you need synthetic aperture radar to pierce cloud cover. You also need a satellite with a high-resolution camera—and software-defined solutions that enable it to identify the overcast spots and avoid collecting useless data there—to identify equipment in transit.

You’re feeling the pressure, as every minute the situation on the ground is unfolding. Meanwhile, you know the adversary’s cyber force might be attempting to penetrate the ground station’s aging infrastructure or take the system down altogether. 

The good news for operators is that data engineering enables software advances that provide new capabilities and flexibility. According to the Department of Defense (DOD) Data Strategy that was released in September 2020, success begins with data-centricity, putting data at the center of the organization and its systems—enabling the U.S. to act on data faster than its adversaries. This approach can be summarized as meaning the network must accommodate the data rather than the other way around.

For ground systems, this shift can be achieved by building open-architecture systems that can adapt for changing missions. DOD’s September 2023 Space Policy directive emphasizes the need to accelerate the transition to more resilient architectures, and DOD mandates modular open-system approaches.

Open-architecture systems allow for built-in zero trust security, providing granular access controls while enabling continuous change. Implemented by technologists who understand the intricacies of the mission, they allow space agencies to build a system around core goals while keeping capabilities flexible to adapt for future priorities and challenges. 

Let’s return to the example where you’re the operator of a satellite observing troop movements below. Instead of being in a decades-old facility juggling multiple screens, you’re now at a remote location, working on your laptop.

Thanks to virtualization and automation, you’re experiencing a very different scenario. The virtual ground system has alerted you to unusual troop movement in the area and provides a list of commercial satellites that are available to be tasked for Earth observation, along with key insights such as the cost for using each one, the image fidelity you can expect, and the dwell time that you need to factor in. Finally, it provides a two-part recommended course of action:

• Task a commercial Earth observation satellite and a classified radar satellite to do a flyover.
• Notify allies in the area of troop movement and its coordinates. You review the recommendation and press Execute.

The virtual system automatically executes both parts of the recommendation. Within minutes, naval vessels nearby move toward the area in a show of force, and the adversarial troops, caught off guard by the rapid action, begin to disperse.

This is just one example of how smart, scalable, secure virtual ground systems can help humans enable the one thing that’s better than winning a war: avoiding conflict altogether through effective deterrence. 

• The proliferation of small satellites is fueling demand for cloud-based ground systems that allow space agencies to adopt advanced capabilities and avoid the cybersecurity risks of brick-and-mortar facilities.
• A virtual space ground system using a multicloud platform and plug-and-play architectures makes it possible to manage space missions on a single laptop and can reduce the satellite tasking process from days to hours—or even minutes.
• With virtual ground systems, open data frameworks enable organizations to leverage machine learning operations (MLOps)—the ability to build, train, and tune AI models for insights on what has happened and how to respond.