稀疏宽带信号亚奈奎斯特采样与重构算法及其在宽带频谱感知中的应用研究

项目名称： 稀疏宽带信号亚奈奎斯特采样与重构算法及其在宽带频谱感知中的应用研究

项目编号： No.61501150

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 盖建新

作者单位： 哈尔滨理工大学

项目金额： 19万元

中文摘要： 无线电通信、电子侦察等领域信号带宽的不断提高，使得高采样率成为信号处理系统发展的一个瓶颈。亚奈奎斯特采样方法的提出，为缓解稀疏宽带信号采样率高的压力提供了新的解决思路。本项目针对调制宽带转换器(MWC)亚奈奎斯特采样方法中重构算法性能不高、测量矩阵构建方法研究不充分、分布采样时缺少高效联合重构方法、与实际应用的融合研究较少等问题展开研究。结合压缩感知和多测量向量重构方法提出高性能的MWC重构算法；充分利用多个MWC获取的数据中存在的联合稀疏性和信息互补优势，探索多MWC分布式亚奈奎斯特采样联合重构算法；研究完整亚奈奎斯特采样系统的实现方法，基于激励响应思想提出不依赖于系统具体实现的测量矩阵构建方法；利用采样率低并可实时重构的优势，结合信息融合理论，提出基于MWC的认知无线电宽带频谱感知快速方法。本项目的研究成果将进一步完善亚奈奎斯特采样理论并推动其在应用方面的发展。

中文关键词： 亚奈奎斯特采样；压缩感知；稀疏重构；多测量向量问题；频谱感知

英文摘要： The signal bandwidth in the fields of radio communication, electronic reconnaissance etc is constantly increasing, which makes the high sampling rate become a bottleneck hindering the development of signal processing systems. The newly proposed sub-Nyquist sampling method provides a new solution to alleviate the pressure from high sampling rate of sparse wideband signals. This project is aimed at the problems in the sub-Nyquist sampling method based on Modulated Wideband Converter (MWC), including unsatisfactory performance of the recovery algorithm, insufficient research in terms of measurement matrix construction, the lack of an efficient joint recovery method for distributed sampling, the lack of fusion with practical applications and so on. The latest achievements of compressive sensing and the multiple measurement vector recovery problem will be combined to propose a new high-performance recovery algorithm. A joint recovery algorithm for distributed MWC sub-Nyquist sampling will be explored by making full use of the joint sparsity and the advantage of complementary information in data acquired from multiple MWCs. The method of using existing electronic components to achieve complete sampling system is planned to be investigated sufficiently, and a construction method of the measurement matrix will be presented by studying the stimulus-response relation of the system. MWC-based wideband spectrum sensing methods for cognitive radio will be put forward by using the advantage of low sampling rate and real-time recovery and combining with information fusion theory. The outcome of this project will improve existing sub-Nyquist sampling theory and promote its development towards applications.

英文关键词： Sub-Nyquist Sampling;Compressive Sensing;Sparse Recovery;Multiple Measurement Vector Problem;Spectrum Sensing