Abstract: A RF Environment Learning Module includes a machine learning/artificial intelligence sensing controller process that schedules feature vector extractors and one or more machine learning (ML) signal classifiers in order to classify received radio signals and a ML-based validation process that reasons over the classification results to determine if they are valid or reasonable.

Abstract: Systems and methods are provided for distributed monitoring of radio frequency electromagnetic spectrum using dedicated and/or opportunistic sensors and varying sensor tasking assignments to collect, and optionally process collected data, that is used in conjunction with previously stored data and/or a plurality of models such as propagation models, terrain models, etc. Spectrum usage is sensed at multiple locations to obtain spectrum data, and integrated the spectrum data to form an information product that can be provided to a requestor, such as a secondary user of a region of spectrum.