Threat Emitter Omniscience Suite (THEOS)

In this project, ISL will develop advanced Threat Emitter Omniscience Suite (THEOS) capability in support of the most critical Air Force missions including Intelligence, Reconnaissance, & Surveillance (ISR) and Electronic Warfare (EW) with a focus on Suppression of Enemy Air Defenses (SEAD) against near-peer Integrated Air Defense Systems (IADS). THEOS will characterize adversary IADS from single and/or multiple airborne platforms using unique and robust terrain-induced identifiers from Radio Frequency (RF) In-phase and Quadrature-phase (IQ) data. This is a significant advancement from traditional identifiers like frequency, pulse width, transmit power, pulse repetition frequency (PRF), etc. that can be easily adapted by the adversary to confuse the threat characterization algorithms. As highlighted by Russia’s war on Ukraine [1], deployment of combat aircraft in the battle is extremely challenging without achieving superiority over the IADS. This necessitates innovative and adaptive solutions to characterize threats and provide actionable information for maximizing the survivability of our airborne assets. THOES addresses a key goal stated in the United States Air Force Science and Technology Strategy for 2030 and beyond is to “Increase the speed of battlespace understanding and decision-making to act faster than any adversary.”  THEOS will play a key role in rapid and effective decision making for our airborne assets, Intelligence Community, and Commanders. The new techniques developed under THEOS are targeted to characterize systems employing modern Active Electronically Scanned Arrays (AESA) that exhibit highly agile scanning, beam switching, cognitive, and waveform diversity capabilities. We will incorporate advanced antenna models beyond standard phased arrays to demonstrate robustness of the proposed approach. The emitter characterization will include advanced pulse sorting, geolocation (latitude, longitude, height) as well as emitter mode and behavior characterization for complex multi-function (MFR) multi-mode RF systems. Additionally, algorithms will be developed to characterize cognitive and fully adaptive radars as well as multiple input multiple output (MIMO) radars from the time-series measurements using both terrain-induced identifiers as well as other system parameters. We will leverage our high-fidelity physics-based site-specific modeling and simulation (M&S) tool RFView® to generate realistic IQ data necessary to support this project. In this project, we will generate extensive datasets using M&S in rich multipath environments for a multitude of AF relevant missions.