Managing the data deluge: How military radar systems are getting smarter
StoryFebruary 12, 2025
Every second, military radar systems collect terabytes of data about potential threats in the skies above. But having data isn’t the same as having intelligence. In an age of information overflow, the U.S. Department of Defense faces a new kind of challenge: turning this tsunami of radar data into actionable battlefield insights.
The military stakes couldn’t be higher: Air and missile defense systems must detect, track, and respond to threats in real time. With hypersonic missiles, drone swarms, and other sophisticated threats becoming increasingly common, radar operators need to process and analyze massive amounts of data faster than ever before. A delay of even a few seconds in converting raw radar data into actionable intelligence could mean the difference between a successful intercept and a catastrophic failure.
Data deluge
Today’s military radar systems collect enormous amounts of data. Sorting through this mountain of information quickly enough to be useful in combat is the big problem facing industry.
The first hurdle is simply moving all this information around. “With direct digitization and wide bandwidth sensors, radar front-ends are producing more data that needs to be communicated to the back-end processor,” says Matt Alexander, chief engineer for sensor systems at Mercury Systems (Andover, Massachusetts). “This is driving the need for high-speed fabrics such as 100/400 Gbit Ethernet over fiber.”
Traditional processing power just isn’t enough anymore. “Even traditional radar processing becomes more computationally complex due to the increase in data,” he continues. “This is driving the need for the highest-performing processors.”
Carl Nardell, principal engineering fellow at Raytheon (Tucson, Arizona), says that while radar systems generate massive amounts of data, “most of this data is not very useful.” The solution? Process it immediately, he says. “The more we can process data at the point of collection, the more tractable the problem becomes.” (Figure 1.)
[Figure 1 | Raytheon’s Lower Tier Air and Missile Defense Sensor (LTAMDS) radar features three arrays that can detect and track multiple aerial threats simultaneously, including hypersonic weapons. (Image courtesy Raytheon)]
Industry is racing to develop smarter ways to handle this information. Dr. Justin Pearson, senior director of architecture and business growth in aerospace and defense at Wind River (Alameda, California), says that the most promising technology includes edge computing, high-performance data compression, and cloud integration for managing massive real-time data flows.
The role of artificial intelligence
Artificial intelligence (AI) is quickly becoming a useful tool for turning all this radar data into useful battlefield information. By processing information faster than humans and spotting patterns that might otherwise be missed, AI is changing how military forces collect and analyze radar data.
One of the big ways AI can help is that it is capable of spotting things that traditional processing methods might miss, Alexander says.
“AI has the ability to improve sensor effectiveness by leveraging the increase in data and by exploiting features in data not exploitable by conventional processing techniques,” he says.
He points out that AI could help with several key tasks: better filtering out enemy jamming, identifying the difference between real threats and false alarms, identifying what kind of object it’s seeing, and keeping track of targets in confusing situations and cluttered environments.
The crucial advantage of AI systems is speed. Nardell notes that “a single graphics processing unit (GPU) can perform millions of times more analysis operations than a human.” This amped processing power means faster decision-making in critical situations.
“By using AI, we can automate intelligence analysis to provide useful insights in seconds rather than hours, days or weeks,” he adds. “AI has the ability to massively scale up human operations. AI can help process reams of data into actionable intelligence and accurate targeting information at speed and scale in high-risk environments.”
However, there are limits to how AI can be used in military systems. Pearson notes that it’s still early days for AI, and it can’t be used yet in safety-certified elements. However, AI can still be helpful by helping radar systems work in difficult conditions, he says.
“[AI can] recognize and classify targets using image recognition and radar signal processing, with deep learning models identifying objects like vehicles, aircraft, or drones even in degraded environments such as heavy jamming or low visibility,” Pearson explains.
AI is also proving valuable in countering enemy actions. “AI will be able to more effectively make use of multifunction apertures,” Alexander says. “Dynamic scheduling of sub-apertures is a complex problem that AI will be able to optimize.”
Other data processing solutions
While AI currently gets most of the attention, defense industry engineers are looking at other new ways to handle the massive amounts of radar data produced every day. One promising approach involves using light instead of electricity to process data.
“While digital signal processing remains a foundational component to radars, novel photonic and analog processors are being developed to process – on operationally relevant timescales – large quantities of data,” Alexander says. “We are seeing an increasing interest in photonic interconnects and inclusion of photonic processors.”
Combining data from multiple sources is another strategy. “Data fusion, particularly across multiple platforms and multiple sensor types, assists in reducing false alarms and discriminating decoys,” he explains. (Figure 2.)
[Figure 2 | The Mercury Systems HDS6605 is a 6U OpenVPX server blade featuring Intel's 2nd-generation Xeon scalable processor for edge-processing applications. (Image courtesy Mercury Systems)]
The challenge is similar to those faced in other technological areas, Nardell says. “Electronic warfare and sensor data analysis are fundamentally becoming big data problems,” he says. “The data is simply too large and complex to manage or process using traditional methods.”
Nardell believes traditional computer science offers some solutions to the data glut, pointing to relational databases, parallel compute techniques, and edge processing as tools that could help with the data-processing challenge.
High-performance computing is another important tool, Pearson says, as it can perform large-scale data processing through parallel computing, accelerating simulations, real-time decisions, and predictive modeling (such as in combat scenario planning).
Delivering intelligence to the battlefield
Edge computing is particularly promising. Processing data near the battlefield slashes latency and bandwidth needs, allowing for real-time analytics even in remote environments, Pearson says, noting that radar systems are able to analyze data locally “to trigger alarms without waiting for centralized systems.”
Raytheon is working to deploy GPUs at the point of intelligence collection to enable important, useful data to be refined and routed to the warfighter quickly, Nardell says.
“We now have the ability to process data at the point of collection with compute power that had only been available in a datacenter, mainframe, or server,” he says.
The goal is to reduce large amounts of data into useful information that can be easily shared, which “allows a very small amount of transferred data to impact the OODA [observe, orient, decide, and act] loop of an adjacent platform or even commander,” Alexander says.
The military can test these systems during training to ensure they work in combat. “Agreeing on what data and insights must be shared can be proven out in training exercises and thus ensure a robust networked effect during a future conflict,” Alexander adds.
Local storage systems are also important when communications are cut off. Pearson points to promising new tech like distributed storage systems with local caching that enable offline access during disruptions, as well as ruggedized tablets and augmented-reality goggles that can provide real-time mission insights. (Figure 3.)
[Figure 3 ǀ Local collection, processing, and storage systems can be deployed to deliver intelligence to the edge. Stock image.]
The role of open standards
Open standards like the Sensor Open Systems Architecture, or SOSA, Technical Standard are changing how the defense industry approaches radar system development and procurement, making it easier to integrate new technologies and work with multiple vendors.
“Open standards widen the pool of candidate technology and capability providers,” Alexander says. “For instance, some open standards allow for the insertion of 3rd-party radar modes. This type of model enables dozens of organizations to develop and integrate radar mode IP in addition to the radar OEM [original equipment manufacturer], making the radar a best-of-breed system.”
Software containers have been particularly helpful in implementing these standards.
“Containerized software has been the biggest enabler for drawing upon the best AI capabilities from any source,” Nardell says. “The standardization of hardware has commoditized compute capability such as GPUs, enabling the latest and best hardware to be applied to this area without updating algorithmic software.”
At the end of the day, using SOSA aligned parts just makes it easier for systems to work together, Pearson says.
“The SOSA [approach] provides well-defined, standardized interfaces that enable seamless integration of hardware and software components from different vendors, ensuring that radar, sensor, and computing systems can exchange data securely and operate collaboratively, reducing proprietary lock-ins,” he says.
The modular nature of parts aligned with the SOSA Technical Standard also makes it easier to upgrade systems. “SOSA encourages modular components that are easily upgraded or replaced, allowing new capabilities like advanced data encryption modules, AI accelerators, or storage systems to be integrated without redesigning the entire system,” Pearson continues.
Adherence to open standards also helps the industry in another major way – saving money.
“Open standards lower development costs and extend system lifespan by allowing easy replacement or upgrading of components without requiring overhauls,” Pearson says. “A radar system with SOSA compliant components can receive upgrades to processors or storage modules without major redesigns.”
