Perspecta Labs Wins DARPA Contract to Build an RF Resource Manager for UAVs

Perspecta Labs Wins DARPA Contract to Build an RF Resource Manager for UAVs

Perspecta Labs will be working on phase 3 of the U.S. Defense Advanced Research Projects Agency's (DARPA's) Converged Collaborative Elements for the radio frequency (RF) Task Operations (CONCERTO) program. These awarded contracts build upon successful phase 2 implementation and demonstration and have a combined value of $5.1 million.

The objective of the CONCERTO program is to develop, implement and demonstrate a converged RF resource manager that adaptively controls components of radar, electronic warfare and communications functions on unmanned aerial vehicles (UAVs). During phase 2 of the program, Perspecta Labs' RF Resource Manager significantly outperformed conventional resource management techniques when tested in complex, government-defined scenarios. The technology improved communications throughput, reduced tracking errors and reached unprecedented decision speeds—achieving 100% of mission objectives across these complex scenarios.

Phase 3 work, to be conducted over a 21-month period of performance, will further mature and advance Perspecta Labs' RF Resource Manager, including management of diverse third-party RF payload hardware. The company will also demonstrate the capability, scalability and agility of its solution in-flight tests on a UAV platform to enable technology adoption by other programs of record. The additional contract modification supports a six-month effort to develop distributed extensions of the solution. Petros Mouchtaris, Ph.D., President of Perspecta Labs feels Perspecta Labs' RF Resource Manager changes the paradigm of UAV performance by bringing better, faster, more accurate command and control to warfighters while providing the critical RF dominance needed by the U.S. military to ensure operational success.

Perspecta Labs' innovative RF Resource Manager provides a scalable, extensible and agile solution, leveraging sophisticated machine learning techniques, to meet stringent, real-time performance requirements and achieve complex objectives. It provides management across multiple payloads on a single platform and, with distributed extensions, supports cross-platform distributed battle management applications to enable mission success.

Publisher: everything RF