Radio Frequency Spectrum Patterns of Life

Black River Systems Company is pleased to submit this Phase I SBIR proposal to research, evaluate, and prototype an innovative framework to autonomously detect and bring to an operator’s attention novel and unusual activities in a RF environment. This framework, Autonomous Detection of Anomalous RF-Patterns for Threat-Alerting (ADAPT), can interrogate multiple sensor data streams in near real-time to identify anomalies within the spectrum. Our proposed approach utilizes self-supervised learning to autonomously learn emitter behaviors and RF Patterns of Life (PoL) from the data feeds of existing sensors. These learned RF behaviors and PoL are used to automatically detect anomalous spectrum use in congested environments. The RF spectrum observed by a sensor is often congested and the observed RF PoL are unique to the individual sensor’s location. Our proposed approach is trained in a self-supervised manner (without need for human labeled data) and utilizes online learning to autonomously learn sensor specific RF PoL while continually adjusting to temporal changes in the normalcy RF PoL (daily, weekly, seasonal, etc.). The proposed approach will facilitate near real-time operation without the need for user interaction and alleviate the laborious requirements to label, model, and train RF PoL manually for each sensor emplacement.