When U.S pilots encounter enemy air defenses, onboard electronic warfare (EW) systems protect them by interfering with incoming radar signals – a technique known as electronic attack (EA) or jamming. Conversely, electronic protection (EP) technology prevents hostile forces from using EA methods to disable U.S. radar equipment assets. Defeating hostile radar helps shield aircraft from ground-to-air missiles and other threats, so it's a military priority to ensure that EW systems can defeat any opposing radar technology.
At the Georgia Tech Research Institute (GTRI), which has supported U.S. electronic warfare capabilities for decades, a research team is developing a new generation of advanced radio frequency (RF) jammer technology. The project, known as Angry Kitten, is utilizing commercial electronics, custom hardware development, novel machine-learning software and a unique test bed to evaluate unprecedented levels of adaptability in EW technology. Angry Kitten has been internally funded by GTRI to investigate advanced methods that can counter increasingly sophisticated EW threats.
"We're developing fully adaptive and autonomous capabilities that aren't currently available in jammers," said research engineer Stan Sutphin. "We believe a cognitive electronic warfare approach, based on machine-learning algorithms and sophisticated hardware, will result in threat-response systems that offer significantly higher levels of electronic attack and electronic protection capabilities and will provide enhanced security for U.S. combat aircraft."
When an EW encounter begins, the Angry Kitten system chooses an optimal jamming technique from among many available options, explained Sutphin, who leads a GTRI development team that includes senior research engineer Roger Dickerson and senior research scientist Aram Partizian. As the engagement progresses, the next-generation system is designed to adapt. It will assess how effective its jamming is against the threat and quickly modify its approach if necessary.
Angry Kitten research also includes investigation of cognitive learning algorithms that allow the jammer to independently assess and respond to novel opposing technology. The team is developing techniques to enable an EW system to respond effectively should it encounter unfamiliar hostile radar techniques. Moreover, the flexibility of the Angry Kitten system allows it to represent a range of threat EA systems. That will help to support the development of new and improved EP measures.
Traditionally, Sutphin explained, radar jamming has consisted of two basic approaches. One employs mechanical techniques that reflect radar beams back at the sender using chaff material spread through the air behind the carrying platform. The other uses electronic techniques to emit powerful electromagnetic signals that interfere with incoming hostile radar beams. But these techniques are relatively basic, and they involve overt suppression strategies that are often obvious to the other side.
Today's top EW systems are more subtle, thanks to digital techniques. The most advanced technology today – digital radio frequency memory (DRFM) – can deceive an enemy by recording his received radar signals, manipulating them and sending back false information that seems to be real.
"A DRFM jammer is a very effective way of adding clutter to the scene without just using unsophisticated noise-jamming techniques," Sutphin said. "You can create false targets, or hide real targets, using the enemy's own waveforms against him."
The GTRI team believes that countering such techniques will lead to the development of increasingly more precise digital techniques for radar electronic protection (EP). That could spark an equivalent race for more advanced jammer techniques. In the first phase of developing a next- generation system, the GTRI team completed an advanced jamming system prototype. This custom hardware utilizes a wideband tunable transceiver system and is built using open architecture/open source approaches that are low-cost and enable operators to quickly modify the system in response to changing conditions. The team is currently developing machine-learning algorithms that will allow the Angry Kitten system to continually assess its environment and switch among the best methods for jamming incoming threats. The ultimate goal is a robust platform that will characterize any threat emitter and respond in real time in the most effective way.
Today, DRFM jammers employ a computer-based "library" of known threats that are used to identify and neutralize incoming signals, Sutphin explained. DRFM equipment may also include an electronic-intelligence (ELINT) capability, which monitors and collects information on enemy signals and jammers. The ELINT data gathered may eventually be used – possibly weeks, months, or years later – to improve U.S. threat-response techniques.
"What we want is to perform those same ELINT analysis and adaptive-response tasks in seconds – while the jamming is occurring – not months later," Sutphin said. "And obviously our system must work autonomously, because there's no time for human input."
To support the current effort, the researchers are utilizing a GTRI-designed tool called the enhanced radar test bed. Devised by a team led by Partizian, the test bed simulates opposing radar signals and enables convenient, low- cost and highly realistic testing of jammers. The test bed is an important asset in the development of the Angry Kitten system, Sutphin said. It offers the ability to collect realistic, representative jammer data on advanced waveforms. It can be used to represent virtually any known threat – and even hypothetical radar systems that don't currently exist. The test bed allows the team to rapidly prototype a software approach, test it out against simulated enemy hardware, and come up with high-fidelity data. The researchers can perform this work without having to build or acquire actual hardware radar systems or jammers, or engage in expensive flight tests.