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

宽带无线电发射的检测、识别和定位是射频工程和通信中一个非常相关且具有挑战性的研究课题。根据国际电信联盟（ITU）的规则，无线电监视是代表政府当局进行频谱监管和管理，以监测已分配和监管良好的频段的合法使用情况。未来的频率敏捷认知无线电接入系统甚至会增加其重要性，该系统将对合法分配的空闲频段进行二次利用。最近，无线电监视方法在执法、公共安全、救援工作等方面也越来越重要。我们考虑了一个由空间分布的无线电观测者节点组成的监视网络，这些节点通过通信链路相互连接并链接到数据融合中心，用于数据收集和节点指挥和控制。该节点具有快速宽带频谱时间感知、辐射源探测与识别、到达方向、时间差、接收信号强度等位置相关参数估计等特点。通过数据融合和地理定位方法，获得了某一明确区域内无线电发射的概要视图。主要的研究影响来自于压缩感知方法（CS）的应用。CS是一个数学框架，它允许对稀疏问题进行有效采样，并从待定方程中进行重建。这适用于这里，因为无线电发射在占用的频率、访问时间、地理位置以及调制格式方面都是稀疏的。与传统的奈奎斯特传感相比，CS方法支持的相关数据量大幅减少，更适合观测节点与融合中心之间有限数据速率的通信与合作。这样可以避免信息丢失，否则会影响传感器数据融合。一般来说，CS需要在传感功能设计方面进行范式转变，这将导致宽带天线阵列接收器、位置估计和协作数据采集的全新架构。进一步的创新源于严格纳入多径传播效应，这不仅应被解释为必须减轻的负担。相反，我们将利用多路径作为一种应用关于环境的先验知识的手段，从而增强最终的估计。该项目是德国-哥伦比亚电气工程合作研究计划（GeCoCo-EE）框架内的一部分，该计划基于德国德意志研究集团（DFG）和哥伦比亚科学技术行政部门Innovación （COLCIENCIAS）之间的谅解备忘录（MoU）。

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.