The application of machine learning and artificial neural networks to RF signal processing for the detection and identification of signals of interest and environmental anomalies

A Signal of Interest (SOI) is a signal the operator has decided to record for further analysis. This is driven by mission requirements, known anomaly characteristics, or unidentifiable signals. Currently on our radar detection system, identifying SOI or anomalies is reliant on the system operator's knowledge and skill, a method highly susceptible to human error. The objective was to find a way to provide the system operator with improved awareness by automating identifying SOIs or anomalies with machine learning and artificial intelligence techniques. By applying data science processes and techniques such as density-based clustering algorithms and artificial neural networks to our data, we successfully proved the daily emitter and frequency distribution in the Hampton Roads area has a strong consistent subset of emitter traffic, identified anomalies based on this fingerprint, and implemented this algorithm in an application which provides a graphic that highlights anomalies and SOIs.