复杂电磁环境下基于信号稀疏表示和非周期随机共振的OFDM信号识别与自主接收技术研究

The realization of fast and accurate OFDM signal identification and autonomous reception under low SNR, non-Gaussian and non-stationary complex electromagnetic environment, is one of the research hotspots in the field of cognitive OFDM technology. In order to break the bottleneck of traditional methods, the new theory of the suppression of electromagnetic interference overlapping in frequency domain based on signal reconstruction with cutting over-complete atoms will be explored firstly in this project. Secondly, the stochastic resonance mechanism of OFDM signal, non-Gaussian noise and nonlinear systems will be probed to propose an non-linear detection method for weak OFDM signal. Thirdly, accroding to analysis of the law of cycle of cause and effect for modulation features and parameter, the integrated iteration estimation model for OFDM signal's parameters will be established. Fourthly, the characteristics of the higher order cumulants and higher-order spectrum of the signal under the complex electromagnetic environment will be analyzed, and according to constructing the combination feature quantity which have robustness to noise and multipath fading, the identification of OFDM signal will be realized. Fifthly, the law on impact of time-varying multipath fading and channel noise on the three-dimensional structure of the signal cyclic spectrum and the cyclic autocorrelation function are revealed, and the parameter estimation of signals will be realized by envelop peak searching in the cyclic frequency, frequency and delay section. Finally a simulation platform will be established to admit data of OFDM signal under complex electromagnetic environments and verify the performance of the signal identification and autonomous receiving method. The research of this project will provide a valuable solution for the future cognitive OFDM system in complex electromagnetic environment and it will be of important theoretical significance and practical value.

在低信噪比、非高斯、非平稳的复杂电磁环境下快速准确地实现OFDM信号识别和自主接收是目前认知OFDM技术领域的热点之一。本项目针对传统方法的瓶颈，探究基于过完备原子裁剪信号重构的频域重叠非平稳电磁干扰抑制的新理论；探索OFDM信号、非高斯噪声和非线性系统随机共振的物理机理，提出一种微弱OFDM信号非线性检测方法；分析OFDM信号调制特征与参数之间的因果循环关系，建立参数综合估计的迭代层次结构模型；分析复杂环境下信号高阶累积量和高阶谱的特性，通过构建对多径衰落和噪声鲁棒的组合特征量实现OFDM信号识别；揭示多径衰落和噪声对信号循环谱和循环自相关函数的三维结构影响规律，通过在循环频率、频率和时延切面的搜索实现信号的参数估计；最后搭建仿真平台录取复杂电磁环境下OFDM信号数据，验证信号识别和自主接收的性能。项目为复杂电磁环境下认知OFDM系统提供一种有价值解决方案，具有重要的理论意义和实用价值。

OFDM技术作为一种频谱利用率高、抗多径衰落和脉冲干扰能力强和实现成本低廉的多载波调制技术，已经在民用领域和军事领域中得到广泛应用。从无线电频谱检测、认知无线电、通信侦察和电磁战的角度出发，对低信噪比、非高斯、非平稳的复杂电磁环境下未知OFDM信号进行识别和自主接收具有重要价值。项目组对申请书中列出的各项研究内容开展了深入的研究和探索，主要研究概况和取得的成果如下：.1）提出了一种频域重叠单载波干扰信号抑制的制方法：该方法根据OFDM信号和频域重叠单载波干扰信号之间形态成分的差异性，对不同的信号采用不同的学习字典进行稀疏表示，提出了一种基于学习字典和符号同步信息的频域重叠单载波干扰信号抑制方法；仿真结果证明该方法相比于传统的频域置零法能更有效地降低系统的误码性能。.2）探索微弱OFDM基带复信号、背景噪声和双稳非线性系统在每个OFDM符号周期期间的肼内随机共振机理，提出一种基于符号周期分段和双稳肼内随机共振的OFDM信号处理和解调方法；该方法符合数字通信信号的解调物理过程，而且每次随机共振处理的信号样本数据量小，大大地降低了随机共振处理的运算量。.3）分析OFDM信号调制特征与各个参数之间的因果循环关系，建立了OFDM信号参数综合估计的迭代层次结构模型。.4）为了解决低信噪比条件下OFDM信号与单载波信号、小波包(WPM)信号间的调制识别问题，提出了一种基于分层迭代支持向量机（SVM）的OFDM信号调制识别方法；分析复杂环境下信号高阶累积量和高阶谱的特性，提出了一种基于利用分层迭代SVM分类器OFDM信号的调制识别方法，通过高阶累积量和双谱图获取特征向量，采用分层迭代的识别结构，提高了低信噪比下OFDM信号识别率，并且克服了以往在有限训练样本数据及待识别数据非线性情况下的局限性。.5）揭示多径衰落和噪声对信号循环谱和循环自相关函数的三维结构影响规律，通过在循环频率、频率和时延切面的搜索实现信号的参数估计。.6）搭建采集平台，录取实际场景下OFDM信号（无人机空地链路信号）数据，在计算机上仿真平台验证OFDM信号识别和自主接收的性能。.项目为复杂电磁环境下认知OFDM系统提供一种有价值解决方案，具有重要的理论意义和实用价值。