雷达辐射源信号时频图像处理研究
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摘要
雷达辐射源信号分类识别是现代电子侦察和电子支援系统的重要研究内容,也是衡量电子对抗设备先进程度的重要标志。近年来,随着电子战的激烈对抗,复杂体制雷达辐射源信号的分类识别已成为电子对抗领域的关键技术和难题。时频分布作为一种新的现代信号处理技术,将一维辐射源信号转换到二维时频信号,不但能够反映辐射源信号能量随时间和频率的分布,而且能揭示其频率随时间的变化关系,为辐射源信号的特征分析和分类识别提供了重要信息。但复杂体制雷达辐射源信号调制规律复杂,且在传播和处理过程中易受噪声干扰,信噪比变化较大,使得在时频分布平面对雷达辐射源信号的分析处理变得极其困难。
为此,本文围绕低信噪比条件下,复杂体制雷达辐射源信号时频分布的处理问题,首先将雷达辐射源信号的时频分布表示为灰度图像,然后通过图像处理技术对复杂体制雷达辐射源信号的时频图像进行处理,主要研究复杂体制雷达辐射源信号时频图像预处理、辐射源信号时频图像特性分析以及基于时频图像特征的雷达辐射源信号分类方法。论文的主要工作及研究成果具体如下:
1.综合分析了复杂体制雷达辐射源信号的数学模型,针对几种典型雷达辐射源信号,采用Wigner-Ville分布及Cohen类时频分布对其进行了时频分析,并给出了相应的仿真实验结果分析。
2.针对传统图像增强方法存在的一些不足,提出一种基于粗集理论的图像增强方法。仿真实验及结果分析表明,该方法在有效地抑制噪声的同时,更好地保护了图像的边缘和细节信息,增强了图像的对比度,是一种更有效的图像增强方法,在增强效果和时间复杂度方面均优于传统方法;并将该图像增强方法应用于辐射源信号的时频图像增强处理,取得了较好的效果。
3.针对常用时频分析方法对多分量辐射源信号处理的一些不足,在分析辐射源信号时频分布的基础上,将辐射源信号的时频分布看作灰度图像,利用图像处理技术中的平滑滤波、阈值处理和形态学细化算法,研究一种基于图像处理技术的多分量辐射源信号时频图像处理方法。相应的仿真实验结果表明,该方法在有效抑制噪声的同时,能够得到具有高分辨率的多分量辐射源信号时频图像,更有利于多分量辐射源信号时频特性分析,既克服了常用时频分析对多分量辐射源信号时频分析的不足,又优于时频重排方法。
4.将神经网络应用于雷达辐射源信号的时频图像处理,并在处理后的时频图像中实现了辐射源信号调制参数的估计,提出一种基于神经网络的多分量信号时频图像处理方法。仿真实验表明,在低信噪比情况下,该方法能够对辐射源信号的时频图像进行有效地处理,并能较准确地提取辐射源信号各分量的调制参数。
5.针对复杂体制雷达辐射源信号的分类问题,在对辐射源信号时频图像特征分析的基础上,研究基于时频图像特征的辐射源信号分类新方法。该方法将辐射源信号分类问题转换为图像处理及识别问题,先对辐射源信号进行时频分析,获得时频分布图,并将其转化为灰度图像和作归一化处理,再用支持向量机对处理后的图像进行分类。通过对5种典型辐射源信号的分类仿真实验表明,该方法信噪比高于2.5dB时,平均正确分类率达92%以上。
The classification of radar emitter signals is an important research task in modern electronic reconnaissance and electronic support system, and also determines the technical merits of electronic reconnaissance equipment. Recently, with the countermeasure activities in modern electronic warfare becoming more and more drastic, the classification of the advanced radar emitter signals has become the crucial technique and the difficulty in signal processing of electronic warfare. As a novel modern signal processing technology, time-frequency distribution that transforms time-domain signals into two-dimension time-frequency signals, not only reflects the distribution of emitter signal energy in time and frequency plane, but also reveals the change of emitter signals' frequency with time, so it provides the important information for the feature analysis and the classification of emitter signals. But the time-frequency distribution is very complex for the advanced radar emitter signals and easily disturbed by noise in the process of transmitting and processing. Moreover, the Signal-to-Noise Rate (SNR) of emitter signals is always changeable. So it is very difficult for the processing of radar emitter signals in time-frequency domain.
Therefore, considering the processing problem of advanced radar emitter signals time-frequency distribution at low SNR, this thesis discusses the time-frequency distributions of radar emitter signals as a grayscale image, and then processes the time-frequency image of advanced radar emitter signals using image processing technology. The dissertation mainly studies the time-frequency image pre-processing, the time-frequency analysis of advanced radar emitter signals and the classification methods for radar emitter signals based on time-frequency image features. The main work and research fruits are as follows.
1. The mathematical model of advanced radar emitter signals is analyzed systematically. Considering several typical radar emitter signals, the Wigner-Ville distribution and Cohen's time-frequency distribution are used for time-frequency analysis of the emitter signals and the corresponding experimental results are given.
2. To solve the limitations of traditional image-enhancing methods, a novel method for image enhancement is presented based on rough set theory. Experimental results show that the introduced method can remove noise effectively, and preserve the details of image edges to a certain degree and improve the contrast. This method is superior to traditional methods in terms of image enhancement effects and time complexity. And then this image enhancement method is adopted to enhance the time-frequency image of the advanced radar emitter signals and the experimental results are satisfying.
3. To overcome the limitations of conventional time-frequency analysis methods for processing multi-component radar emitter signals, a novel method for time-frequency images processing of multi-component radar emitter signals is proposed based on image processing technology in this dissertation. Based on analyzing the time-frequency distribution of multi-component radar emitter signals, the method regards the time-frequency distribution of emitter signals as grayscale images, and makes use of the spatial smoothing operation, the threshold comparison and the thinning algorithm of morphology. Simulation results show that the introduced method can remove noise effectively, and improve the time-frequency image's resolution. The method is quite suitable for analyzing the multi-component radar emitter signals in time-frequency domain and it is superior to conventional time-frequency analysis and the time-frequency reassignment methods.
4. Using neural network to process time-frequency image and extracting the modulation parameters of emitter signals in the time-frequency image, this dissertation presents a method for time-frequency image processing of multi-component advanced radar emitter signals. Simulation results demonstrate that the method can effectively process the time-frequency image and can exactly extract modulation parameters of multi-component emitter signals at low SNR.
5. To correctly classify advanced radar emitter signals, this dissertation analyzes time-frequency image features and presents a novel method for classifying radar emitter signals based on time-frequency image features. This method transforms the classification of emitter signals into image processing and image recognition. Emitter signals are analyzed in time-frequency domain and their time-frequency images are obtained. These images are transformed into grayscale images and normalized. Support vector machines are applied to design classifiers for recognizing the processed images, which corresponds to different radar emitter signals. Experiments conducted on five typical emitter signals show that the introduced method achieves more than 92% correct classification rate on condition that the signal-to-noise ratio is above 2.5dB.
为此,本文围绕低信噪比条件下,复杂体制雷达辐射源信号时频分布的处理问题,首先将雷达辐射源信号的时频分布表示为灰度图像,然后通过图像处理技术对复杂体制雷达辐射源信号的时频图像进行处理,主要研究复杂体制雷达辐射源信号时频图像预处理、辐射源信号时频图像特性分析以及基于时频图像特征的雷达辐射源信号分类方法。论文的主要工作及研究成果具体如下:
1.综合分析了复杂体制雷达辐射源信号的数学模型,针对几种典型雷达辐射源信号,采用Wigner-Ville分布及Cohen类时频分布对其进行了时频分析,并给出了相应的仿真实验结果分析。
2.针对传统图像增强方法存在的一些不足,提出一种基于粗集理论的图像增强方法。仿真实验及结果分析表明,该方法在有效地抑制噪声的同时,更好地保护了图像的边缘和细节信息,增强了图像的对比度,是一种更有效的图像增强方法,在增强效果和时间复杂度方面均优于传统方法;并将该图像增强方法应用于辐射源信号的时频图像增强处理,取得了较好的效果。
3.针对常用时频分析方法对多分量辐射源信号处理的一些不足,在分析辐射源信号时频分布的基础上,将辐射源信号的时频分布看作灰度图像,利用图像处理技术中的平滑滤波、阈值处理和形态学细化算法,研究一种基于图像处理技术的多分量辐射源信号时频图像处理方法。相应的仿真实验结果表明,该方法在有效抑制噪声的同时,能够得到具有高分辨率的多分量辐射源信号时频图像,更有利于多分量辐射源信号时频特性分析,既克服了常用时频分析对多分量辐射源信号时频分析的不足,又优于时频重排方法。
4.将神经网络应用于雷达辐射源信号的时频图像处理,并在处理后的时频图像中实现了辐射源信号调制参数的估计,提出一种基于神经网络的多分量信号时频图像处理方法。仿真实验表明,在低信噪比情况下,该方法能够对辐射源信号的时频图像进行有效地处理,并能较准确地提取辐射源信号各分量的调制参数。
5.针对复杂体制雷达辐射源信号的分类问题,在对辐射源信号时频图像特征分析的基础上,研究基于时频图像特征的辐射源信号分类新方法。该方法将辐射源信号分类问题转换为图像处理及识别问题,先对辐射源信号进行时频分析,获得时频分布图,并将其转化为灰度图像和作归一化处理,再用支持向量机对处理后的图像进行分类。通过对5种典型辐射源信号的分类仿真实验表明,该方法信噪比高于2.5dB时,平均正确分类率达92%以上。
The classification of radar emitter signals is an important research task in modern electronic reconnaissance and electronic support system, and also determines the technical merits of electronic reconnaissance equipment. Recently, with the countermeasure activities in modern electronic warfare becoming more and more drastic, the classification of the advanced radar emitter signals has become the crucial technique and the difficulty in signal processing of electronic warfare. As a novel modern signal processing technology, time-frequency distribution that transforms time-domain signals into two-dimension time-frequency signals, not only reflects the distribution of emitter signal energy in time and frequency plane, but also reveals the change of emitter signals' frequency with time, so it provides the important information for the feature analysis and the classification of emitter signals. But the time-frequency distribution is very complex for the advanced radar emitter signals and easily disturbed by noise in the process of transmitting and processing. Moreover, the Signal-to-Noise Rate (SNR) of emitter signals is always changeable. So it is very difficult for the processing of radar emitter signals in time-frequency domain.
Therefore, considering the processing problem of advanced radar emitter signals time-frequency distribution at low SNR, this thesis discusses the time-frequency distributions of radar emitter signals as a grayscale image, and then processes the time-frequency image of advanced radar emitter signals using image processing technology. The dissertation mainly studies the time-frequency image pre-processing, the time-frequency analysis of advanced radar emitter signals and the classification methods for radar emitter signals based on time-frequency image features. The main work and research fruits are as follows.
1. The mathematical model of advanced radar emitter signals is analyzed systematically. Considering several typical radar emitter signals, the Wigner-Ville distribution and Cohen's time-frequency distribution are used for time-frequency analysis of the emitter signals and the corresponding experimental results are given.
2. To solve the limitations of traditional image-enhancing methods, a novel method for image enhancement is presented based on rough set theory. Experimental results show that the introduced method can remove noise effectively, and preserve the details of image edges to a certain degree and improve the contrast. This method is superior to traditional methods in terms of image enhancement effects and time complexity. And then this image enhancement method is adopted to enhance the time-frequency image of the advanced radar emitter signals and the experimental results are satisfying.
3. To overcome the limitations of conventional time-frequency analysis methods for processing multi-component radar emitter signals, a novel method for time-frequency images processing of multi-component radar emitter signals is proposed based on image processing technology in this dissertation. Based on analyzing the time-frequency distribution of multi-component radar emitter signals, the method regards the time-frequency distribution of emitter signals as grayscale images, and makes use of the spatial smoothing operation, the threshold comparison and the thinning algorithm of morphology. Simulation results show that the introduced method can remove noise effectively, and improve the time-frequency image's resolution. The method is quite suitable for analyzing the multi-component radar emitter signals in time-frequency domain and it is superior to conventional time-frequency analysis and the time-frequency reassignment methods.
4. Using neural network to process time-frequency image and extracting the modulation parameters of emitter signals in the time-frequency image, this dissertation presents a method for time-frequency image processing of multi-component advanced radar emitter signals. Simulation results demonstrate that the method can effectively process the time-frequency image and can exactly extract modulation parameters of multi-component emitter signals at low SNR.
5. To correctly classify advanced radar emitter signals, this dissertation analyzes time-frequency image features and presents a novel method for classifying radar emitter signals based on time-frequency image features. This method transforms the classification of emitter signals into image processing and image recognition. Emitter signals are analyzed in time-frequency domain and their time-frequency images are obtained. These images are transformed into grayscale images and normalized. Support vector machines are applied to design classifiers for recognizing the processed images, which corresponds to different radar emitter signals. Experiments conducted on five typical emitter signals show that the introduced method achieves more than 92% correct classification rate on condition that the signal-to-noise ratio is above 2.5dB.
引文
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