EXPEDITION TECHNOLOGY, INC. — Department of Defense SBIR Phase I: N221-073

Description

The RF spectrum is a dense and dynamic radio frequency (RF) environment. Traditionally, the limited number and relatively slow evolution of signals in the environment allowed signal intelligence, electronic support, and electronic warfare systems to emphasize specific signals of interest (SOIs) with little risk of surprise. Increasingly, civilian, commercial, and national users of spectrum compete for bandwidth using a rich and complex array of evolving waveforms, radio technologies, and access schemes. It is no longer feasible to know in advance how, when, where, and with which signals an adversary may transmit. Instead, new and unusual “anomalous” uses of spectrum must be identified and evaluated alongside traditional SOIs. To address this need, we propose Osiris, an autonomous sensor processing framework able to detect and characterize previously unknown signals and spectrum use patterns, learn new signal detectors in the field, use RF fingerprints to sort and associate transmissions, identify emitter behaviors, correlate spectrum activities to build RF scene understanding, and optimize RF sensor tasking. Recent advances in the processing of RF spectrum using artificial intelligence and machine learning-based approaches have demonstrated their suitability to a wide range of RF processing tasks, from raw spectrum characterization to reasoning over large temporal, spectral and spatial scales. The objective of this program will be to develop and demonstrate a hierarchical RF pattern of life learning capability suitable to detect anomalies and other atypical uses of RF spectrum.