【作者】 常成；

【导师】 宫新保；

【作者基本信息】 上海交通大学 ， 信号与信息处理， 2009， 硕士

【摘要】 如今高概率的雷达有源欺骗式干扰信号检测是雷达抗干扰过程的基础,但是目前我军的雷达有源干扰信号的检测和识别在很大程度上还依赖于雷达操作员的经验,在复杂的电子战环境中,要做到实时正确地检测是否存在干扰对雷达操作员难以实现。因此,研究有源干扰的智能监测和识别,不仅可以消除人为因素的不利影响,还可以提高识别率,增强雷达的对抗能力,具有极其重要的军事意义。基于以上考虑,本文首先在已有工作的基础上,创新地提出了基于免疫遗传算法的神经网络分类器,实现了对雷达有源干扰信号的自动高精度的分类和识别;同时,首次提出了一种针对脉冲多普勒雷达欺骗式干扰的硬件可实现实时检测系统,以应用于实时性能要求较高的电子攻防战中;最后,本文设计了该实时检测系统的硬件,软件模块,并完成了相关的系统设计工作。本文的主要工作可以概括如下:1)本文创新地提出了基于免疫遗传算法的神经网络分类器,实现对雷达有源干扰信号的自动分类识别算法:首先从信号处理的角度出发,研究分析了雷达有源干扰信号在时域、频域和其它变换域上的特征,提取了多个特征参数,从不同角度采用不同方法研究了雷达有源干扰信号的特征。之后结合本文提出的基于免疫系统的径向基函数神经网络用于实现对信号特征的分类以判断是否存在欺骗式雷达干扰信号。仿真验证和性能分析后并通过比较发现,该免疫神经网络分类器精度性能优于目前已有的基于神经网络的其他分类器性能,同时结合免疫算法,又具有较好的时间复杂度。2)本文针对目前雷达欺骗干扰信号检测算法实时性能普遍不高的情况,提出了一种针对脉冲多普勒雷达的欺骗式干扰的硬件可实现的实时检测方法:针对目前常见的脉冲多普勒雷达和常见的DRFM体制的欺骗干扰信号,研究了相应的信号特征并提出了一种可以硬件实现的实时欺骗式干扰检测方法。仿真表明该方法能够有效的检测出针对脉冲多普勒雷达的距离项、速度、加速度上的欺骗式干扰信号。3)本文在提出实时处理算法的基础上,进行了硬件软件设计并完成了最终的硬件系统设计:针对提出的算法,结合现有的硬件平台和手段考虑,选择相应的硬件配置并设计出了相应的硬件平台,就其中的主要关键问题进行了实现详细的硬件设计和软件设计,实现了相应的硬件平台并进行了测试。更多还原

【Abstract】 Active deceive jamming plays a crucial role in modern electronic warfare. The objective of detection and identification is to decide the type of active deceive jamming signal without any priori knowledge about the enemy, and then take the corresponding measures to suppress the jamming. The detection and identification of active deceive jamming is studied in this dissertation. First, an improved radar active deceive jamming signal detection algorithm is proposed, then a real-time radar active deceive jamming signal detection method is proposed to detect DRFM deceive jamming signal, At last, The hardware system is implemented. The main work of this dissertation is summarized as follows:1. A neural network classifier based on immune system is proposed for radar active deceive jamming signal detection after signal characters extraction: First different feature extraction methods are used to explore the characteristics of active deceive jamming signal. Then RBFNN based on immune system is studied to recognize the active deceive jamming automatically. The validities and superiorities of two classifiers are shown in the experiments, and several conclusions are obtained: The RBFNN can perform the identification of active jamming signal effectively also it is simple to implement and has a good performance.2. Based on the study of Pulse Dropper radar and DRFM active deceive jamming signal, a engineering method is proposed to detect DRFM active deceive jamming towards Pulse Dropper radar in this dissertation. Simulation results show that the method can effectively detect DRFM active deceive jamming signal.3. According to the DRFM active deceive jamming detection method and current hardware implement technology, the radar active deceive jamming detection system was implemented. Hardware system based on ADC and FPGA was designed and software of the system including spectrum estimation was also designed. Experiments show that the hardware system can effectively detect DRFM active deceive jamming signal.更多还原