Compressed Localization and Spectrum Sensing for Cognitive Radio and Distributed Radio Surveillance (CLASS)

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Detection, identification, and localization of wideband radio emissions is a very relevant and challenging research topic in RF engineering and communications. According to the rules of the International Telecommunication Union (ITU), radio surveillance is carried out on behalf of governmental authorities for spectrum regulation and management to monitor legal usage of the allocated and well regulated frequency bands. Importance will even increase with future frequency agile cognitive radio access systems that will make secondary usage of legally allocated, but idle bands. Recently, radio surveillance methods are gaining increased relevance also for law enforcement, public safety, rescue work, etc. We consider a surveillance network of spatially distributed radio observer nodes that are mutually connected and linked to a data fusion center by communication links for data collection and node command and control. The nodes feature fast wideband spectral-temporal sensing, emitter detection and identification, and location related parameter estimation such as direction and time difference of arrival as well as received signal strength. A synoptic view of radio emissions within a certain well defined area is gained by data fusion and geolocalization methods. The major research impact comes from the application of compressed sensing methods (CS). CS is a mathematical framework that allows efficient sampling of sparse problems and reconstruction from underdetermined equations. This applies here since the radio emissions are sparse in occupied frequency, access time, geolocation, and also in terms of the modulation format. Compared to conventional Nyquist sensing, CS methods support a considerable reduction of the relevant data volume, which suits better for limited data rate communication and cooperation between the observer nodes and the fusion center. This avoids loss of information, which would compromise sensor data fusion. In general, CS requires a paradigm shift in sensing function design which will lead to completely new architectures for wideband antenna array receivers, location estimation, and for cooperative data acquisition. Further innovation results from the strict inclusion of multipath propagation effects that shall not only be interpreted as a burden which has to be mitigated. Instead, we will exploit multipath as a means of applying a priori knowledge about the environment and, hence, to enhance the final estimation. This project is embedded within the Framework of the German-Colombian Collaborative Research Initiative in Electrical Engineering (GeCoCo-EE), which is based on the Memorandum of Understanding (MoU) between Deutsche Forschungsgemeinschaft e.V. (DFG), Germany and Departamento Administrativo de Ciencia, Tecnologia e Innovación (COLCIENCIAS), Colombia.