Intelligence, Surveillance and Reconnaissance Exploitation

DARPA’s Strategic Technology Office (STO) is focused on technologies that enable fighting as a network to increase military effectiveness, cost leverage, and adaptability.

DARPA’s Strategic Technology Office (STO) is focused on technologies that enable fighting as a network to increase military effectiveness, cost leverage, and adaptability. STO's areas of interest include: Battle Management, Command and Control; Communications and Networks; Intelligence, Surveillance, and Reconnaissance; Electronic Warfare; Positioning, Navigation, and Timing; and Foundational Strategic Technologies and Systems.

Current airborne electronic warfare (EW) systems must first identify a threat radar to determine the appropriate preprogrammed electronic countermeasure (ECM) technique. This approach loses effectiveness as radars evolve from fixed analog systems to programmable digital variants with unknown behaviors and agile waveforms. Future radars will likely present an even greater challenge as they will be capable of sensing the environment and adapting their transmissions and signal processing to maximize performance and mitigate interference effects.

The Arctic region is poised for greater regional significance as polar ice retreats in coming decades. Ship traffic likely will increase during summer months, and commercial activity focused on the sea floor is expected to grow. The Arctic is largely isolated, vast, and environmentally extreme. Remote sensing may offer affordable advantages over traditional methods of monitoring the region—aircraft, satellites or manned ships and submarines—due to the great distances in the Arctic.

The Behavioral Learning for Adaptive Electronic Warfare (BLADE) program is developing the capability to counter new and dynamic wireless communication threats in tactical environments. BLADE is enabling a shift from today's manual-intensive lab-based countermeasure development approach to an adaptive, in-the-field systems approach. The program will achieve this by developing novel machine-learning algorithms and techniques that can rapidly detect and characterize new radio threats, dynamically synthesize new countermeasures, and provide accurate battle damage assessment based on over-the-air observable changes in the threat.