Electromagnetic Warfare
Local Inputs are Critical to Capability

Electromagnetic warfare (EW) is entering a different phase. Spectrum denial remains an important tactic, but it is no longer the full measure of advantage. The sharper contest targets decision quality under electromagnetic pressure. When signals, links and tracks become uncertain, commanders may still have platforms available but lose confidence in the information they are presented with.

This shift in the nature of EW is in turn fuelling the concept of cognitive electromagnetic warfare, which integrates artificial intelligence and machine learning to enable controlled adaptive rapid responses to suspicious electromagnetic signals.

The automated threat detection and response system combines sensing, signal interpretation, spectrum awareness and feedback into a disciplined learning cycle. In a primarily defensive doctrine, as in the UAE context, the cycle helps determine what is changing in the spectrum, what information remains reliable, and whether EW responses are producing the intended effect.

Recent conflicts show that military advantage is increasingly generated through connected kill chains rather than isolated platforms. Sensors, datalinks, navigation support, weapons, decoys and electronic protection all form part of an ever-more complex equation.

A recent air engagement in an adjacent region highlighted the operational value of networked off-board sensing, cueing and engagement geometry. Long-range drone campaigns in other theatres show the same logic at lower cost. One-way attack systems are therefore being upgraded with improved navigation resilience, onboard processing, remote links and terminal guidance features. These adaptations make basic disruption less decisive. A defender may jam one link while the wider chain continues through another. But the commander still has to identify which parts of the chain remain functional and whether EW responses are adequate.

The US places spectrum superiority at the centre of operations in contested environments, treating electromagnetic spectrum operations as an activity conducted across the joint force. European capability planning gives weight to electromagnetic order of battle and dynamic spectrum management. East Asian strategies link electromagnetic capability with cross-domain operations and unmanned systems.

The common direction is clear: electromagnetic operations are a decisive part of force design. This means armed forces need to interpret spectrum activity, manage uncertainty and support
command decisions under electromagnetic pressure. Better EW equipment is necessary, but this also has to sit inside a wider architecture for sensing, command and operational assurance.

The challenge is that while the technology is rapidly advancing, its performance can still be uneven. Machine learning can help classify signals, identify emitters, sense spectrum activity and support fingerprinting. It can improve recognition and prioritisation; however, performance still depends on data quality, signal conditions, receiver differences and adversary behaviour.

Open-set recognition is an area of concern. A deployed EW system cannot assume that every relevant signal is already in its library. It may encounter modified emitters, altered waveforms, protected datalinks or deceptive emissions. Recent open-set and open-world radio frequency (RF) fingerprinting work treats unknown emitters as a central problem.

Robustness is another constraint. RF models can degrade when receivers change, channels shift or signal quality falls. They can also be affected by spoofed examples, poisoned data and deliberate changes in emission behaviour. In EW, the adversary is actively shaping the signal environment. Accuracy alone is therefore insufficient.
Confidence amid uncertainty and resilience against deception become operational requirements. Cognitive electromagnetic warfare requires RF engineering, signal processing, machine learning, threat
libraries, operator judgement and controlled experimentation.

The field is moving in the right direction, but it still needs stronger open-set performance, better robustness, local validation and reliable impact assessment.

So, what are the capability implications for armed forces? Quite simply, there must be a sovereign electromagnetic learning capability that begins with a local evidence base. Imported models and vendor-tested systems may remain useful, but their performance cannot be assumed under difficult operating conditions. Local RF, global navigation satellite systems (GNSS) and interference
data must form the foundation of sovereign capability.

The next requirement is reliable interpretation. Cognitive electromagnetic warfare has limited value if it only recognises known signals under expected conditions. It must also detect unfamiliar behaviour, express confidence appropriately and indicate whether contested information remains reliable enough to support action. This is where data becomes operational judgement.

That judgement supports decisions across all domains. The EW response interacts with air defence, communications, unmanned systems, maritime security and critical infrastructure. Spectrum decision-support must therefore be considered part of operational risk management, not a technical add-on.

The final requirement is feedback. EW responses must be assessed. Operators need to know whether an action disrupted the target, forced a mode shift, displaced the threat or generated unintended effects. Without that, EW remains episodic. With it, the force begins to learn.

The near-term objective is clear: establish the evidence, interpretation, validation and feedback layers before moving toward controlled adaptive EW responses.