But on a more serious note the use of ‘Loyal wingmen’ is a serious move forward in Electronic Warfare. Until the last century traditional combat methods were the norm. Attacks and defence, soldier to soldier, ship to ship, plane to plane and the various combinations. Electronic warfare had made an appearance in at least the beginning of the 20th century. The earliest documented consideration of EW during the Russo-Japanese War of 1904–1905 and grew from then on. As technology became more available and affordable, adversaries grew their EW capabilities. Now, these are growing more sophisticated and... unpredictable, due to their ability to "learn" and adapt.
With AI, intelligent machines work and respond a lot like humans, but machines are performing smarter tasks, faster. Machine learning takes AI one step further, allowing machines to continuously learn from data and adapt as a result. Computers are learning at a very rapid rate. Threats using machine learning learn from every encounter, determining ways to be more effective so that they can overcome countermeasures.
This is happening without human interaction, as the computer decides how to alter behaviours When tested or engaged, these threat systems learn from that experience then modify their future behaviour, ie the computer is deciding the next steps. Due to the system’s unpredictable behavior, even people responsible for the system cannot predict its exact behaviour.
As threat systems advance with machine learning technology, they adapt and alter their behaviour/course of action at an increasingly rapid rate. An example might be a radar trying to track a jet. The adversary’s countermeasures may stop it from succeeding but using machine learning the radar would repeatedly try new approaches to overcome these. Today’s machines possess intelligence that is an order of magnitude higher than a human expert in EW and as they learn that data continues to aggregate.
Responsive threats already exist, often called cognitive and adaptive. Although these terms are used interchangeably, 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 an adversary’s capabilities then by doing something that makes the adversary’s system react, they evaluate its response, quickly understand the scenario, then develop an effective response suited for that particular adversary’s system.
In contrast, adaptive solutions cannot rapidly grasp and respond to a new scenario in an original manner. An adaptive radar can sense the environment then alter transmission characteristics, providing a new waveform for each transmission or adjusting pulse processing. This flexibility allowing 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 behaviour leaving adversaries trying to isolate adaptive radar pulses from other signals and whether it’s friend or foe
But the EW domain is just beginning to implement machine learning and AI. These technologies and their applications will evolve rapidly resulting in increasingly complex threats and 'loyal wingmen’ with AI will become increasing needed on the battleground, air land or sea.
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