【作者】 李辉；

【导师】 金炜东；

【作者基本信息】 西南交通大学 ， 控制工程（专业学位）， 2017， 硕士

【摘要】 电子侦察是电子战的先导和基础,是决定战争胜负的重要因素。如何在复杂的电磁环境中利用的情报为电子战提供支援,从而提高电子战能力,已成为现代电子战中亟待解决的关键问题。本文以空间运动平台间的对抗为研究背景,主要研究机载相控阵雷达工作模式识别问题。拟从相控阵雷达的基本原理出发,对机载相控阵雷达四种典型的工作模式进行仿真和分析,并采用深度神经网络对不同工作模式的信号特点进行学习和识别,为电子对抗技术的发展提供支持。本文的具体研究工作如下:1、根据雷达基本工作原理,建立机载相控阵雷达模型,并根据不同的运动场景对雷达的典型工作模式进行仿真和分析。仿真涉及相控阵雷达边搜索边跟踪、搜索加跟踪、多目标跟踪、单目标跟踪四种工作模式;通过仿真产生不同雷达工作模式下侦察机截获的脉冲描述字,为后续工作模式的分析和识别提供基础。2、针对雷达天线扫描特性分析容易受到噪声干扰的问题,提出基于多层次建模的信号幅值拟合方法。该方法对脉冲描述字进行脉冲等级、脉冲组等级、工作模式等级建模,有效克服了漏脉冲和虚假脉冲对信号幅值拟合的影响。3、提出基于去噪自动编码器的雷达工作模式识别方法,并深入分析了深度神经网络中隐含层数、节点数、噪声注入比例等参数对网络性能的影响;在此基础上比较了不同深度神经网络模型的识别效果。通过实验对基于去噪自动编码器的工作模式识别方法的有效性进行验证,结果表明:该方法不仅能自适应地提取到信号中区别不同工作模式的本质特征,而且对噪声环境具有较好的适应能力,为雷达工作模式识别提供了一种新的思路。4、针对去噪自动编码器训练耗时长的缺陷,研究了基于边缘去噪自动编码器的工作模式识别方法;并对结合SVM和DS证据理论的信息融合方法进行研究,进一步提高分类正确率。实验结果表明:基于边缘去噪自动编码器与DS证据理论相结合的算法能有效地识别出雷达不同的工作模式,不仅具有较好的特征表达能力,而且能有效降低分类错误率。更多还原

【Abstract】 As the precursor and foundation of electronic warfare,electronic reconnaissance is an important factor in determining the outcome of the war.How to use the intercepted intelligence in the complex electromagnetic environment to provide support for the electronic warfare,so as to improve the ability of electronic warfare,has become a key issue to be solved in modern electronic warfare.Taking the confrontation among space movement platforms as our research background,we mainly study the recognition of airborne phased array radar under different work mode.Based on the basic principle of radar,four typical work modes of airborne phased array radar are simulated and analyzed,and the deep neural network is used to learn and identify the characteristics of signal,hoping this work can provide support for the development of electronic countermeasure technology.The main work of this paper are as follows:1.According to the basic principle of radar,the model of airborne phased array radar is established to simulate and analysis typical work modes of radar,according to different intercept scene.The simulation contains four kinds of work modes such as track while scan,track and scan,multi-target tracking and single target tracking.Simulation results are the pulse description words intercepted by the reconnaissance aircraft,which provide basis for work mode analysis and recognition of airborne phased array radar.2.A signal amplitude fitting method based on multi-level modeling is proposed to solve the problem that the characteristics of the radar scaning are easily interrupted by noise.Multi-level modeling method is used to model the pulse description words at pulse level,pulse group level and the work mode level,effectively overcome the influence of dropped or false pulse on amplitude fitting.3.The method based on denoising autoencoder is proposed to recognize work mode of airborne phased array radar.Also we make a through analysis of the parameters belong to deep neural network,such as the number of hidden layers,the number of hidden nodes and the ratio of noise.On this basis,the recogniton rate of different deep neural networks are compared.Experimental results show that our method can not only extract the essential characteristics of different modes adaptively,but also has a good ability to adapt to the noise,which provide a new idea for recognition of radar.4.In view of the disadvantage of denoising autoencoder,costing lots of training time,a new method based on marginalized stacked denoising autoencoder is proposed.Also an information fusion method combining SVM and DS evidence theory is proposed to further improve the classification accuracy.Qualitative experiments show that the proposed method combining marginalized stacked denoising autoencoder and DS evidence theory,can not only learn more useful representations,but also achieve lower test error,which verified its the effectiveness.更多还原