F-22Raptor
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The proliferation of advanced digital radar systems is focusing military research and development efforts on technology that can identify and respond to unknown threat emitters in real time, principally through the use of artificial intelligence.
Adaptive EW tools enable forces to detect, identify and defeat radars that utilise agile waveforms and can change their signal characteristics by switching between frequencies to unknown characteristics in order to evade detection.
By applying advanced AI algorithms to EW tools, electronic countermeasure (ECM) and electronic support measure (ESM) systems are able to automatically respond in real time to enemy radar without the need to re-programme the system for each new signal characteristic detected.
Previously ESM and ECM solutions relied solely on pre-programmed databases of known signals, which then determined if the signals detected are friend or foe and whether to use pre-programmed countermeasures. However, as radar technology has advanced, ECM tools have had to respond in type.
The US Defense Advanced Research Projects Agency (DARPA) currently has two major programmes focused on the development of adaptive EW solutions to protect forces from enemy sensors that could be used for missile targeting.
Under one of these programmes, Adaptive Radar Countermeasures (ARC), the US Navy selected a Leidos and Harris team system that will automatically generate countermeasures against unknown and adaptive radar emitters. The system will be integrated onto the navy’s fleet of F/A-18 Super Hornet aircraft.
According to DARPA the Leidos-developed system uses AI and machine learning processes to isolate unknown radar signals and detect them amongst other friend, foe and neutral signals. It can deduce the threat posed by the radar, transmit countermeasure signals and then assess the effectiveness of the countermeasures.
DARPA’s second on-going programme is the Behavioural Learning for Adaptive Electronic Warfare (BLADE) project focused on developing countermeasures against new, dynamic wireless communication threats.
AI is expected to radically improve the speed at which threats can be detected, identified and responded to. Under the BLADE programme, new machine learning algorithms and techniques are being developed that are able to characterise and counter signals designed to jam communication systems.
The US Army is also fielding new handheld EW prototype kits, developed by the Rapid Capability Office (RCO), to forces in Europe to reinforce their defences against Russian EW systems.
The current systems deployed with US troops were delivered under Phase 1 of the programme and include ESM and ECM tools. As the systems continue to be developed tools with enhanced capabilities including AI will be delivered to units in the field.
‘The Army will continue phased delivery of upgraded electronic warfare capabilities while adding emerging technologies such as artificial intelligence and machine learning,’ Douglas Wiltsie, director of the Army RCO told Shephard.
While advances in signal processing and AI tools have enabled the development of adaptive EW systems that can respond to today's emerging threats, next generation technologies including quantum radar could be resistant to such solutions.
In development with advanced military nations, including China and North America, a quantum radar offers the potential to overcome some of the most advanced ECM and stealth techniques.
https://www.shephardmedia.com/news/...europe-2018-us-focuses-ai-driven-adaptive-ew/
Adaptive EW tools enable forces to detect, identify and defeat radars that utilise agile waveforms and can change their signal characteristics by switching between frequencies to unknown characteristics in order to evade detection.
By applying advanced AI algorithms to EW tools, electronic countermeasure (ECM) and electronic support measure (ESM) systems are able to automatically respond in real time to enemy radar without the need to re-programme the system for each new signal characteristic detected.
Previously ESM and ECM solutions relied solely on pre-programmed databases of known signals, which then determined if the signals detected are friend or foe and whether to use pre-programmed countermeasures. However, as radar technology has advanced, ECM tools have had to respond in type.
The US Defense Advanced Research Projects Agency (DARPA) currently has two major programmes focused on the development of adaptive EW solutions to protect forces from enemy sensors that could be used for missile targeting.
Under one of these programmes, Adaptive Radar Countermeasures (ARC), the US Navy selected a Leidos and Harris team system that will automatically generate countermeasures against unknown and adaptive radar emitters. The system will be integrated onto the navy’s fleet of F/A-18 Super Hornet aircraft.
According to DARPA the Leidos-developed system uses AI and machine learning processes to isolate unknown radar signals and detect them amongst other friend, foe and neutral signals. It can deduce the threat posed by the radar, transmit countermeasure signals and then assess the effectiveness of the countermeasures.
DARPA’s second on-going programme is the Behavioural Learning for Adaptive Electronic Warfare (BLADE) project focused on developing countermeasures against new, dynamic wireless communication threats.
AI is expected to radically improve the speed at which threats can be detected, identified and responded to. Under the BLADE programme, new machine learning algorithms and techniques are being developed that are able to characterise and counter signals designed to jam communication systems.
The US Army is also fielding new handheld EW prototype kits, developed by the Rapid Capability Office (RCO), to forces in Europe to reinforce their defences against Russian EW systems.
The current systems deployed with US troops were delivered under Phase 1 of the programme and include ESM and ECM tools. As the systems continue to be developed tools with enhanced capabilities including AI will be delivered to units in the field.
‘The Army will continue phased delivery of upgraded electronic warfare capabilities while adding emerging technologies such as artificial intelligence and machine learning,’ Douglas Wiltsie, director of the Army RCO told Shephard.
While advances in signal processing and AI tools have enabled the development of adaptive EW systems that can respond to today's emerging threats, next generation technologies including quantum radar could be resistant to such solutions.
In development with advanced military nations, including China and North America, a quantum radar offers the potential to overcome some of the most advanced ECM and stealth techniques.
https://www.shephardmedia.com/news/...europe-2018-us-focuses-ai-driven-adaptive-ew/
