Compressed Localization and Spectrum Sensing for Cognitive Radio and Distributed Radio Surveillance

在未知或部分未知的无线电环境中检测、识别和定位无线电辐射的任务在许多应用中是至关重要的。在这个项目中，我们考虑了一个高度灵活的分布式无线电监测系统，可以部署为CR网络中的传感系统或用于执法，公共安全和监管目的的被动无线电监测和无线电监测系统。所考虑的系统由配备有感测能力的不同组合（一个或多个）的空间分布感测节点组成，例如，宽带频谱感测、DoA估计、用于TDoA估计的GPS同步等，传统的频谱感知、发射源定位和信号智能方法是基于传统的奈奎斯特速率采样和频率、时间和空间处理。这通常转化为对传感器硬件和处理能力以及通信链路所需吞吐量的严格要求。最近出现的压缩传感（CS）的范例提供了一个数学框架，同时感测和压缩的信号，稀疏或可压缩的表示，这是经常在考虑的系统中的情况。CS在数据收集和处理的各个阶段的应用允许放松SN处的硬件要求并减少交换的信息量。然而，CS框架仍然严重缺乏关于模拟信号的亚奈奎斯特采集方法和从接收到的压缩样本进行信号处理的知识，这些方法考虑了包括对噪声的鲁棒性在内的实际约束。本项目的主要目标是弥合这一差距，并研究基于CS的方法，在现实的信号和传播conditions.In项目的第一阶段，我们已经应用在传感器级根据所考虑的传感器类型的亚奈奎斯特采样的分布式无线电surveillance。此外，我们还研究了每个系统任务中基于CS的信号处理操作的原理。在第二阶段，我们考虑的方法，在一个传感器和传感器数据融合的压缩联合时间/频率-空间信号定位的几个传感能力的集成。此外，我们还根据新的发现扩展了第一阶段开发的基于软件定义无线电的概念验证演示。该项目是德国-哥伦比亚电气工程合作研究计划框架内嵌入的CLASS项目的延续（GeCoCo-EE），该协议基于Deutsche Forschungsgemeinschaft e. V.（DFG），德国和哥伦比亚市政、技术和创新部。

The tasks of detection, identification, and localization of radio emissions in unknown or partially unknown radio environments are crucial in a number of applications. In this project, we consider a highly agile system for distributed radio surveillance that can be deployed as a sensing system in CR networks or a system for passive radio monitoring and radio surveillance for law enforcement, public safety and regulatory purposes. The considered system is comprised of spatially distributed sensing nodes equipped with different combinations (one or more) of the sensing capabilities, e.g., wideband spectrum sensing, DoA estimation, GPS synchronization for TDoA estimation, etc., and a central unit for control and data processing connected to the nodes via communication data links.Traditional approaches for spectrum sensing, emitter localization and signal intelligence are based on the conventional Nyquist rate sampling and processing in frequency, time, and space. This often translates into stringent requirements on the sensor hardware and processing capabilities as well as required throughput of communication links. Recently emerged paradigm of compressed sensing (CS) provides a mathematical framework for simultaneous sensing and compression of signals that have sparse or compressible representation that is often the case in the considered system. The application of CS at various stages of data collection and processing allows relaxing the hardware requirements at the SNs and reducing the amount of exchanged information. CS framework however still has a significant lack of knowledge on the sub-Nyquist acquisition methods for analog signals and signal processing from received compressed samples that take into account practical constraints including robustness to noise. The main goal of this project is to bridge this gap and investigate CS-based approaches for distributed radio surveillance under realistic signal and propagation conditions.In the first phase of the project, we have applied sub-Nyquist sampling at the sensor level according to the considered sensor types. Additionally, we have investigated principle CS-based signal processing operations within each of the system tasks. In the second phase, we consider approaches for an integration of several sensing capabilities in one sensor and sensor data fusion for compressive joint time/frequency-spatial signal localization. Furthermore, we extend Software Defined Radio-based proof-of-concept demonstrations developed in the first phase according to the new findings.This project is a continuation of the CLASS project 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.