Multiple Deep Spectrum Usage Models (MUDSUM)

As the electromagnetic spectrum becomes increasingly crowded, it is critical that US ground force and tactical Signals Intelligence (SIGINT) and EW sensors are able to quickly and automatically scan large swaths of the RF spectrum and make sense of usage by private, commercial, civil and military entities operating a given area. Machine understanding of patterns of both routine and anomalous signal behavior would provide an indispensable advantage to operators monitoring and analyzing spectrum usage, allowing them to focus their efforts on only the most meaningful and actionable detections. The MUDSUM is an ML/AI system using a modular architecture which integrates deep signal detection, deep signal characterization, and a suite of pattern of life modeling methods, with domain relevant subject matter expertise. MUDSUM will be able to model dynamics in single emitters, multiple emitters of different classes, and additionally use context aware methods which model current behavior of the spectrum given the recent past. This multitiered and multiaspect method provides insight into typical and atypical behavior from multiple angles giving operators a large amount of visibility into the state and projected state of spectrum usage. MUDSUM’s Phase I development will culminate in a proof of concept demonstration, evaluating the suite of signal detection, characterization, and behavior modeling tools.