Electronic Warfare (EW) has long been a contest of adaptation—jamming, deceiving, and countering signals present in the electromagnetic spectrum. But today’s battlespace moves too fast for human operators alone. Signals shift frequencies mid-pulse, radar systems hop across bands, waveforms are dynamic, and small fractions of seconds make the difference between survival and defeat. This is where cognitive electronic warfare (cognitive EW) comes in.
As Dr. Michael Simon, VP of Advanced Technology at Parallax Advanced Research and the Ohio Aerospace Institute, explains, cognitive EW “takes the person out of the loop, because the timescales to react are very fast. You don’t have seconds—you may only have milliseconds, or in some cases microseconds.”
Caption: Dr. Michael Simon, VP of Advanced Technology at Parallax Advanced Research and the Ohio Aerospace Institute
From Traditional to Cognitive EW
Traditional EW relies on humans to analyze the electromagnetic environment and decide on countermeasures. Cognitive EW, by contrast, leverages heuristics, classical and contemporary Artificial Intelligence (AI) based on neural networks like Machine Learning (ML), Deep Neural Networks (DNNs) and Spiking Neural Networks (SNNs) to sense signals, characterize them automatically, and respond in real time.
These types of cognitive EW systems can defend against adversary radar tracking and targeting, block adversary communication links, and even mislead adversary sensors—without waiting for human decision-making. In essence, cognitive EW transforms EW from a primarily reactive discipline into an adaptive, proactive capability.
The enabler of cognitive EW are cognitive systems based on AI that can generate solutions at the speed of battle. Modern digital EW systems operate on In-Phase/Quadrature (IQ) samples—digital representations of analog signals. AI can process this data faster and more effectively than traditional digital signal processing.
By combining heuristics, ML, Large Language Models (LLMs), and even generative techniques, AI-enabled systems can both:
1. Classify signals in the digital domain, including those never encountered before.
2. Generate tailored response signals to counter or deceive adversary systems.
This speed and adaptability open doors not just to defense, but also to offensive EW.
As Simon said, “Instead of just preventing enemy radar from locking onto an aircraft, we could start fooling that radar into seeing other aircraft in places where none exist.”
An Evolving Threat Landscape
Adversaries are no longer using static waveforms. Modern systems dynamically shift frequencies, sometimes even within a single pulse. Cognitive and adaptive EW solutions shine here, countering threats that would otherwise outpace fixed countermeasure libraries.
Cognitive EW is not limited to cutting-edge neural networks. It uses a layered toolkit:
Classical heuristics for fast, rules-based responses.
Reinforcement and deep learning trained offline on simulated and emulated threat data.
Online learning for systems that adapt in the field, even when sample sizes are limited.
Neural networks also demonstrate a capacity to generalize—to detect, classify, and counter signals they weren’t explicitly trained on. When pushed beyond their training regimes, emerging tools like LLMs and generative approaches may extend adaptability even further.
Challenges in Data and Trust
Cognitive EW isn’t without hurdles. Training requires massive datasets—hundreds of thousands of representative signals that capture the “fingerprints” unique to each transmitter. Since real-world data is hard to collect at scale, researchers often simulate or emulate threat signals. Getting those simulations right is critical to ensuring robust AI models.
Another challenge: trust. Operators must understand why AI systems act as they do. Simon envisions using LLMs as “explainers,” providing higher-level reasoning for decisions. While operators may not always like the answer, explainability builds trust and accountability in critical missions.
Human in the Loop—or On the Loop?
The speed of modern waveforms makes it impractical to keep humans fully in the loop. Increasingly, operators are “on the loop,” overseeing automated systems rather than directing them step by step. Fighter pilots, for example, may rely on automated countermeasures against radar locks, while larger EW brigades in the field may still require human judgment.
Risks of Over-Reliance
AI-enabled EW introduces new vulnerabilities. Adversaries could exploit real-time online training, feeding poisoned data that causes systems to “learn” the wrong responses. Preventing such adversarial attacks requires new safeguards—an area where research is still maturing.
Near- and Long-Term Applications
In the near term, radar defense is the most promising application. By defeating tracking and targeting radars, cognitive EW could allow aircraft to operate effectively without costly stealth capabilities.
In the long term—perhaps 50 to 100 years—Simon foresees fully autonomous EW agents operating without human intervention. The challenge is not as technical as it is doctrinal. We need to determine situations where we deem it acceptable to take the human out of the loop.
“Adversaries aren’t going to worry about human intervention,” he said. “The only way to counter that is to also not have that restriction.”
A Multi-Domain Future
Electronic, cyber, and information warfare are converging. As Simon puts it: “You can deliver cyber effects through EW, and information warfare effects through EW.” Whether disrupting satellite links, injecting false signals, or manipulating broadcast media, the electromagnetic spectrum is the backbone of multi-domain operations.
What It Will Take
Adoption of cognitive EW will require:
Policy breakthroughs to define when human oversight is essential—and when speed must take priority.
Technical advances in edge processing, low-power computing (e.g., neuromorphic and photonic processors), and cost-effective high-frequency digitization.
Secure infrastructure—from innovation centers and anechoic chambers to digital engineering models of threat systems—to test and validate solutions.
The Role of Parallax and OAI
Unlike profit-driven hardware manufacturers, Parallax Advanced Research and the Ohio Aerospace Institute act as neutral conveners and innovators. Their role is to prototype, validate, and transition technologies—bridging the gap between basic research and operational fielding.
Through secure innovation centers, digital engineering, and independent labs, Parallax and OAI provide trusted environments where government, industry, and academia can collaborate. With staff who are former warfighters, they connect operational needs directly to research priorities and prototype demonstrations.
As Simon said, “You need government, industry, and research working together. At Parallax OAI, we interface with all three—and we can act as the fair broker to advance cognitive EW.”
Conclusion
Cognitive EW is not just an upgrade to traditional methods—it is a paradigm shift in spectrum operations. By uniting AI with EW, the U.S. and its allies can stay ahead of adversaries, moving faster, adapting smarter, and integrating across domains.
The future battlespace will not wait for human deliberation. It will be defined by the speed of thought—and by those who can translate disruptive innovation into trusted, operational capabilities.
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About Parallax Advanced Research & the Ohio Aerospace Institute
Parallax Advanced Research is a private advanced research institute that tackles global challenges through strategic partnerships with government, industry, and academia. It accelerates innovation, addresses critical global issues, and develops groundbreaking ideas with its partners. In 2023, Parallax and the Ohio Aerospace Institute, an aerospace research institute located in Cleveland, OH, formed a collaborative affiliation to drive innovation and technological advancements across Ohio and the nation. The Ohio Aerospace Institute plays a pivotal role in advancing aerospace through collaboration, education, and workforce development. More information can be found at parallaxresearch.org and oai.org.