Aerospace & Defense Technology - February 2021 - 18

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measures catch up only to discover that
the threats moved yet another step
ahead. The integration of machine learning (ML) and artificial intelligence (AI)
into EW systems will help to sort
through the vast array of signals and
identify the correct ones on the fly.

Cognitive EW

An EW environment generation architecture that supports multiple hardware types and new technology
insertion is key to keeping pace with rapidly evolving threats and reducing lead times for new systems and
test capabilities. (Credit: Digital Storm/Shutterstock.com)

confuse, disable, or destroy an enemy's
electronic systems. Weapons used for
electronic attack leverage lasers, electrooptical, infrared, and RF technologies.

The Proliferation of New Advanced
Threats
Beyond their multifaceted forms and
capabilities, EW threats boast high intelligence. With the increased use of
adaptive programming, these systems
continue to grow smarter. In response
to observed effects on the battlefield,
they will alter operation via radiated
waveforms, techniques, or timing.
Waveforms, in particular, change nearly
instantaneously. Modern threats are
more adaptable and reprogrammable,
creating an urgent need to characterize
them correctly.
The following are examples of modern warfare:
* Self-propelled decoys
* Jamming a radar using anti-radiation
missiles to foil air defenses
* Electronic deception techniques used to
confuse an enemy's intelligence, surveillance, and reconnaissance (ISR) systems
* Directed energy weapons with the potential to destroy people, materials, and
equipment such as satellites, airborne
optical sensors, and land-based forces.

Modern threats and countermeasures
flood the modern EM spectral environment with thousands of emitters, including radios, wireless devices, and radar
transmissions. This, in conjunction with
advanced digital signal processing (DSP),
creates a dramatically complex electromagnetic spectrum. DSP led to advancements in digital dynamic range and algorithm complexity. This environment
creates complex signal activity, leading
to dynamic and evolving threats for EW
systems. While many EW systems use
technology advancements such as highperformance DSP and gallium nitride
(GaN) amplifiers, the sheer number of
possible scenarios from one threat creates difficult challenges.

Challenges of Analyzing Modern
Electronic Countermeasures
As the pace of technological change
for EW outpaces developmental life cycles, threats evolve more quickly than
the time it takes to build countermeasures. With the near-constant evolution
of threats and signals in EW, militaries
are investing heavily in new technologies to gain a tactical advantage and keep
up with evolving threats. Many militaries find themselves engaged in a headto-head competition, where the counter-

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Cov

ToC

Cognitive EW uses modern machine
learning techniques to increase cognitive
ability, including target recognition, intelligent decision-making, and autonomous
learning. Complex and congested signal
environments make it challenging to locate, identify, jam, and confuse enemy
communications systems - especially if
they are adaptive. For instance, adaptive
radars make it challenging to isolate
pulses from threatening radars, understand threats from hostile radars, and
provide an adequate response.
Military technology is turning to machine learning to create cognitive EW
weapons that can successfully operate
in these environments. These weapons
use software-defined capabilities to gain
operational flexibility in congested (and
contested) environments, quicker up-grades, and greater affordability.
Digital equipment can be programmed
on the fly using software programs, allowing EW solutions like radars and softwaredefined radios to change waveforms and
create unique signatures quickly. As more
communications systems, radios, jammers, and IoT devices operate in the EM
spectrum, spectrum awareness takes on increased importance. New EW systems look
to understand the intent of each system
using the spectrum, rather than relying
upon assumptions about ideal scenarios
regarding the environment, design/application challenge, or hardware like traditional systems. Such assumptions limit the
potential for signal identification and
other tasks, boosted by machine learning.

An Open Architecture to Adequately
Model Threats
A primary challenge for EW systems
is the shortened timeline for countermeasure advances compared to the long
development and upgrade cycles of EW
systems. Traditional EW solutions were
designed to respond to specific, established threats that did not evolve. How-

Aerospace & Defense Technology, February 2021

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Aerospace & Defense Technology - February 2021

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