UAS Based CTSSA for RF Surveillance and Situational Awareness

The future Transparent Battlefield requires advanced spectrum situational awareness for improved threat detection and future tactical Unmanned Aerial System (UAS) applications.Ā Advanced UAS sensor networks will provide continual data and sensor fusion for the future transparent battlefield, and UAS swarms require accurate timing and position information for navigation and information fusion.Ā Thus, advanced spectrum situational awareness requires continuous monitoring by UAS swarms. Traditional signal processing, based on ShannonÆs sampling theorem, does Analog-to-Digital Conversion (ADC) based on uniformly sampled data in the time domain.Ā However, this type of signal processing is done continuously regardless of whether events occurred, and thus wastes the UASÆ limited power to process spectrum that is effectively noise when spectral events over that bandwidth are not present. To achieve long duration continuous UAS surveillance in RF environments, an adaptive event-based sampling method is superior to the traditional uniform sampling method.Ā Event-based sampling is not limited by ShannonÆs sampling theory and instead generates nonuniform samples from analog signals only when an event is detected.Ā Signal processing based on an event-based sampling technique will preserve UAS power by not processing analog signals that do not contain events, thus significantly reducing data processing and energy requirements.Ā Thus, an adaptive, event-based sampling and processing of RF signals is the approach needed for continuous time spectrum situational awareness by UAS platforms. For this SBIR, Nokomis will explore the feasibility of an event-based sampling approach to replace ShannonÆs sampling technique in the ADC process and develop a Convolutional Neural Network (CNN) based classifier, to build a Continuous Time Spectrum Situational Awareness (CTSSA) system for UAS surveillance applications.Ā The technology Nokomis will explore will enhance the ability for UAS networks to conduct long-haul continuous surveillance.