干扰环境下压缩感知：方法及应用

项目名称： 干扰环境下压缩感知：方法及应用

项目编号： No.61471174

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 无线电电子学、电信技术

项目作者： 傅予力

作者单位： 华南理工大学

项目金额： 81万元

中文摘要： 压缩感知是近年来信号处理领域热门研究方向之一。传统压缩感知模型一般仅考虑随机噪声干扰，而同时考虑带有结构化干扰的模型更能具体反映工程实际，且能使处理变得简单。本项目针对此类有干扰环境下的压缩感知，探讨其理论方法，推动它在工程中的应用。 基于工程实际，本项目将研究干扰环境下的压缩感知基本理论与关键技术，主要包括以下几方面的内容：针对干扰的物理机制，研究各种具有工程意义的、有结构的干扰信号，建立新的符合实际的干扰压缩感知模型；针对所构造的干扰模型，研究相应的信号恢复算法；同时，针对新的干扰模型研究稀疏信号的可恢复性基本理论；基于这些研究，形成算法软件包，应用于实际工程。 为同时达到抗干扰、降低采样数与可靠恢复原信号的目的，在研究中将采用空间相关性分析、线性与非线性泛函分析、随机分析、概率统计、数值分析、最优化理论等数学方法和机器学习方法，以期在基本理论和高性能关键技术方面取得有影响力的成果。

中文关键词： 压缩感知；稀疏表示；干扰抑制与分离；相关分析；采样理论

英文摘要： In recent years, Compressed sensing (CS) is one of the hottest research directions in the field of signal processing. In the traditional model of compressed sensing, in general, the random noises are taken into account. However, considering the structural corruption in the model can express the interfered environments in actual engineering more precisely and can simplfy the processing. This proposal wishes to research the theoretical methods in this kind of compressed sensing for corrupted cases. Also, the results of the research will be applied to some engineering fields. In light of engineering practice, this project will study the essential theory and key technology of compressed sensing in this corrupted environment. The research is mainly divided into the following aspects: according to the physical mechanism of interferences, various corruptions with some structures and practical meanings will be considered to construct the new models of compressed sensing that will be almost coincident with the engineering. For the corrupted models, the signal recovery algorithms will be studied. At same time, the recoverability of the algorithms will be discussed in the corrupted environments. Finally, some software package will be made based on our contribution and will be applied to some practices. In order to suppress the corruptions and noises, to reduce the number of the observed measurements, and to ensure the recovery reliably at the same time, in the research, some mathematical methods and machine learning methods will be used, such as the space correlation analysis, linear and nonlinear functional analysis, stochastic analysis, probability and statistics, numerical analysis, optimization theory, and sub-supervised or unsupervised learning methods. The proposal intends some influential achievements both in the essential theory and key technology with high performance.

英文关键词： Compressed Sensing;Sparse Representation;Interference suppression and separation;Correlation Analysis;Sampling Theory