基于压缩感知的机载无源探测系统信息提取与处理技术研究

The primary goals of this project are to propose a new architecture of airborne passive detection system applied to the complicated electromagnetic surroundings; to build a compressive sampling and processing framework, which is implemented easily in engineering, of wideband signals; to find an effectual scheme of extracting and processing the emitting signal’s parameters from data of compressive measurement, based on Compressive Sensing (CS). . The main researches of this project include: the architecture of wideband digital receivers and algorithms of extracting and processing the emitting signal’s carrier frequency based on compressive measurements; algorithms of estimating and tracking the motion emitter’s parameters, such as direction, range and so on, based on compressive measurements; algorithms of extracting, classifying and identifying the pulse compression signal’s interpulse and intrapulse property based on compressive measurements.. By this project, the CS theory and engineering practice can be bridged and the information sensing ability of modern airplane can be enhanced. An effectual scheme can be also provided to intercept, process and identify signals emitted from low probability of interception (LPI) Radar, such as active phased array Radar and so on. At present, relative researches of airborne passive detection system based on CS have become hotspot research topics, and a few of papers in this topic are present at journals. Thus, this project has important significance in theory and applied values in military field. Moreover, it is also an innovative research.

本项目旨在压缩感知理论基础上，构建适用于复杂电磁环境的机载无源探测系统新型结构体系，形成一种物理上容易实现的宽带信号压缩采样与处理框架，寻求从压缩测量中直接提取与处理目标信号各种特征参数的有效方法。. 项目的主要研究内容包括：基于压缩测量的宽带数字接收机结构体系及信号载频信息提取与处理算法；基于压缩测量的运动辐射源方向、距离和运动特征等信息估计与跟踪算法；基于压缩测量的脉冲压缩信号脉间、脉内特征信息提取、分类与识别算法。. 通过此研究，可建立压缩感知理论和实际应用的桥梁，增强先进战机的战场信息感知能力，为截获、处理和识别有源相控阵等低截获概率雷达发射的信号提供有效途径。目前，基于压缩感知的机载无源探测系统的相关研究已成为热点研究课题，在国内外鲜有报道。因此，这项研究不仅具有重要的理论意义和军事应用价值，而且是一项具有开创性的工作。

针对日益复杂的战场电磁环境给现役的机载探测系统带来的挑战，本项目在基于压缩感知的运动辐射源波达方向、极化参数、多普勒频率和信源数目等参数信息估计与跟踪算法；基于压缩感知的脉冲压缩信号脉间、脉内特征信息提取、识别与分离算法；基于神经网络的雷达信号识别、增强与分离算法；宽带数字接收机的弱信号检测以及硬件实现等方面，开展了深入的研究，取得了一批研究成果。其中，在基于稀疏表示的极化辐射源的波达方向和极化参数估计方面，其研究成果已在国际专业学术期刊IET Radar, Sonar & Navigation、Digital Signal Processing、Signal, Image and Video Processing、Radio Science上发表，产生了一定的影响力。在混叠脉冲信号序列的脉间特征信息提取与识别方面，首次将子空间技术与稀疏重构技术相结合，提出了混叠脉冲信号序列的脉间特征信息提取与识别新方法。与传统方法相比较，此方法具有较强的抗缺失和抗噪性能，并适用于持续时间短且变化快的脉冲信号。经实际的电子战数据测试，此方法能够有效提取与识别混叠脉冲信号序列的脉间特征信息。此研究成果已被国际权威学术期刊IEEE Transactions on Geoscience and Remote Sensing录用（待发表）。 目前，此项研究出于刚刚起步阶段，在国内外专业刊物上，相关研究的文献较少。因此，这项研究具有较大学术价值和军事应用价值。