AI Set for Escalating Role in EW Conflicts
2020-12-31 | 5 min read
Until 10 or so years ago, US military leaders operated on the assumption that most adversaries would solely use traditional combat methods. Such attacks are conducted warfighter to warfighter or even ship to ship or plane to plane. Military strategies generally did not include electronic warfare (EW) threats, such as jamming the adversary’s communications or pinpointing their location for a successful attack. As technology became more available and affordable, however, adversaries small to large gained EW capabilities. Now, these threats grow more sophisticated and unpredictable, based largely on their ability to "learn" and adapt.
With AI, intelligent machines work and respond much like humans. Machines can therefore perform smarter tasks using capabilities like signal recognition. Machine learning takes AI one step further, allowing machines to continuously learn from data and adapt as a result. These computers learn over time at a very rapid rate. Threats using machine learning continue to learn from every conflict, determining ways to be more effective so that they prevail against future countermeasures.
This evolution occurs without the need for human interaction, as the computer decides how to alter behaviors. When tested or engaged, these threat systems learn from that experience. They modify their future behavior as a result, which means the computer decides the next steps. Due to the system’s unpredictable behavior, even the people responsible for the system cannot foretell its exact behavior.
As threat systems advance with machine learning technology, they will adapt and alter their behavior or course of action at an increasingly rapid rate. If a radar is trying to track a jet, for example, the adversary’s countermeasures may stop it from succeeding. Using machine learning, that radar would repeatedly try new approaches in an effort to achieve success. Today’s machines possess intelligence that is an order of magnitude higher than a human expert in EW, as they learn from data that continues to aggregate.
Cognitive Vs. Adaptive
Responsive threats already exist, often labeled as cognitive and adaptive. Although people use these terms interchangeably, many levels of adaptability exist. Most of them do not come near the capabilities of cognitive EW. Using machine learning, cognitive EW systems can enter an environment with no knowledge of the adversary’s capabilities and rapidly understand the scenario. By doing something that makes the adversary’s system react, they can evaluate its response. They can then develop an effective response that is suited for that particular adversary’s system.
In contrast, adaptive solutions cannot rapidly grasp and respond to a new scenario in an original manner. For instance, an adaptive radar can sense the environment and alter transmission characteristics accordingly, providing a new waveform for each transmission or adjusting pulse processing. This flexibility allows it to enhance its target resolution, for example. Many adversary systems require only a simple software change to alter waveforms, which adds to the unpredictability of waveform appearance and behavior. Military forces struggle to isolate adaptive radar pulses from other signals - friend or foe.
Such threats are a far cry from threats of the past, which were static in nature – always appearing and behaving the same. Military forces must now assume that a threat might change and prepare to react accordingly. The EW domain is only just beginning to implement machine learning – and eventually, AI. Going forward, these technologies and their applications will greatly evolve. To face the resulting, increasingly complex threats, military agencies will demand flexible, scalable systems that arm countermeasures with the same level of intelligence.
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