Microwave Journal
www.microwavejournal.com/articles/37044-verus-research-announces-5-million-48-month-contract-with-defense-advanced-research-projects-agency

Verus® Research Announces $5 Million, 48-Month Contract With Defense Advanced Research Projects Agency

November 1, 2021

Verus® Research, a New Mexico-based team of scientists and engineers specializing in advanced research and development, announced it has been awarded a $5 million Waveform Agile RF Directed Energy (WARDEN) contract from the Defense Advanced Research Projects Agency (DARPA). If all options of the contract are exercised, the 48-month effort will develop hardware, theory and computational models to extend the range and effectiveness of high-power microwave (HPM) systems.

The contract focuses on electromagnetic interactions with electronics contained within enclosures and the effects on system operation. Verus Research will leverage its background and expertise in researching and modeling the fundamental physical factors which govern the nonlinear back-door interaction of high-power radio frequency energy with complicated electronic systems of interest. The concentrated effects-based effort will help guide the development of new HPM systems, extending their range and effectiveness, and providing new HPM employment approaches for the Defense Department.

“We are honored to receive our first-ever contract award from DARPA,” said Grady L. Patterson IV, chief executive officer of Verus Research. “The contract is extremely important in helping to create more effective technologies and develop new HPM systems and employment approaches for the Department of Defense.  It is a privilege to utilize our more than three decades of experience in this field in creating solutions that matter for our clients.” 

Verus Research will leverage recent test methodologies and modeling approaches and extend them to broader classes of target systems to create a physics-based computational framework for the prediction of HPM effects. The program will provide an integrated approach by using machine learning techniques in conjunction with high-power RF predictive efforts to devise more effective HPM waveforms.