大时宽信号的特征提取及识别方法研究
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摘要
大时宽低截获概率雷达信号由于具有低功率、宽带宽以及良好的抗干扰性和隐蔽性,使得传统的截获接收机很难检测与识别,已广泛应用于雷达、通信等领域。这种具有低截获概率特征的雷达,常用的大时宽脉冲信号主要有:线性调频、相位编码和相参脉冲信号等。目前,不少文献已对常用雷达辐射源的信号识别问题进行了研究,而对关于大时宽低截获概率雷达同频干扰信号的识别与分类方面的研究并不多。
雷达辐射源的有效分类识别是军事自动化控制和指挥系统的强烈需求。对雷达信号细微特征提取、分类和识别,不仅是现代电子对抗侦察机在复杂、密集信号环境下分选雷达信号、高可信度地识别各雷达属性的需要,同时也是雷达系统设计和抗干扰设计的需要。随着雷达技术的发展与应用,现代战场不仅表现在雷达数量的日益增多和电磁环境的日益错综复杂,还体现在电磁频谱的日益拥挤和雷达间出现的同频干扰与带内非同频干扰现象的日益严重。分析和识别低截获概率雷达信号之间的同频/非同频干扰特征,不仅有助于改进雷达体制,提高雷达抗同频干扰能力,而且也有助于雷达区分不同的干扰信号,便于采取不同的抗干扰措施和手段,抑制和保护干扰信号对雷达工作的影响或造成的损害。
为此,本文针对几种典型的大时宽低截获概率雷达信号,研究多同型雷达间同频干扰信号的特征提取与识别问题,并进行了仿真实验,主要包括以下几个方面:
(1)介绍了脉冲压缩的原理以及雷达采用脉冲压缩体制的优缺点。从脉冲压缩原理和截获概率因子入手,研究与模拟仿真了三种典型的大时宽低截获概率雷达信号:LFM、相位编码以及相参脉冲,分别给出了它们的时频特征、模糊函数和低截获概率特性,得出了低截获概率雷达信号具有低峰值功率和频带较宽这两大特点,说明了低截获概率雷达用于目前及未来舰艇及武器装备中的优越性。
(2)分别应用WVD时频分布、分数阶傅里叶变换和分形理论等方法,研究了提取表征LFM、相位编码和相参脉冲这几种大时宽低截获概率雷达信号区别的参数特征和信号特征,得出了应用这些特征的精细分析与识别,可建立雷达信号识别数据库的有效和可行性,为研究现代雷达复杂信号的分选识别和雷达抗干扰措施的运用提供了可靠保证。
(3)针对现代雷达体制下复杂信号的低截获特性,为了提高雷达辐射源信号的个体识别率,提出了一种新的分类识别方法。该方法用小波包变换提取能反映信号脉冲无意调制特征的信号各频带能量,通过泛化能力和学习能力都很强的混合核函数支持向量机进行分类识别,仿真结果证明:该方法是有效和可行的,性能优于已有方法。
(4)针对三种常见低截获概率雷达信号的干扰类型识别问题,结合分形和分数阶傅里叶变换算法,提取干扰后混合信号的信息维数、盒维数以及分数阶域信号分量能量之比,构造特征向量,然后通过支持向量机、灰色关联聚类、以及K-均值聚类积累法对干扰类型分类识别。仿真结果表明:这种方法可以对干扰类型有效分类识别,判断出干扰类型属于同型同频干扰、非同型同频干扰或是带内非同频干扰,对雷达抗干扰设计和系统设计有重要意义。
Large width of the low probability of intercept radar signals which have low power, wide bandwidth and good noise immunity and concealment are hard to be detected and identified, so they have been widely used in radar, communications and other fields. Commonly used in large pulse width are: linear frequency modulation, phase encoding and phase coherent pulse signals.Currently, the study of common radar emitter signal recognition is popular, while the research of classification and identification of LPI radar signals are not many. LPI radar has low power and wide bandwidth, so it can not easily be intercepted and identified by enemy radars, in addition, it has anti-electronic reconnaissance, anti-radiation missiles and anti-jamming techniques and has been widely used in radars, communications and other fields. It gradually becomes an important technology system and working mode in modern radar equipments. Therefore, to study the problem of anti-co-channel interference about LPI radar with large time-width and high duty cycle is of great practical significance and military reference in improving combat capability, maintaining information superiority and increasing battlefield, etc.
Therefore, this article is large for some typical low probability of intercept radar signals wide, research and more of the same type of radar signal interference between the same frequency feature extraction and recognition problems, and simulation experiments, including the following:
(1) Introduced the principle of pulse compression, and the advantages and disadvantages of using radar pulse compression system. From the pulse compression principle and probability of intercept factor to start, research and simulation of the large width of three typical low probability of intercept radar signals: LFM, and coherent phase encoding pulse, respectively, when given their frequency characteristics, fuzzy function and low probability of intercept characteristics, obtained a low probability of intercept radar signals with low peak power and wide band characteristics of these two, indicating a low probability of intercept radar for current and future ships and weapons and equipment in the superiority.
(2) Application of WVD time-frequency distribution, respectively, fractional Fourier transform and fractal theory method to study the extraction characterization LFM, phase encoding and phase coherent pulse width of these types of large, low probability of intercept radar signal characteristics of different parameters and signal characteristics, application of these characteristics obtained detailed analysis and identification of a radar signal recognition can be effective and feasibility of the database for the study of modern radar signal sorting complex identification and the use of radar anti-jamming measures provide a reliable guarantee.
(3) Under the radar system for low interception characteristics of complex signals, in order to improve the individual radar emitter signal recognition rate, a new classification method. This method can reflect the wavelet packet transform to extract the signal modulation characteristics of the signal pulse has no intention of the band energy, through the generalization and learning ability are strong mixtures of kernels support vector machines for classification, simulation results show: the method is effective and possible, performance is better than existing methods.
(4) Low probability of intercept for the three common disturbance radar signal recognition, combined with fractal and fractional Fourier transform algorithm, mixed-signal disturbance extracted information dimension, box dimension and the fractional ratio of the energy domain signal components, structure feature vector, and then support vector machine, the gray relational clustering, and the accumulation of K-means clustering method for disturbance classification. Simulation results show that: This method can effectively classify the type of interference, determine the type of disturbance of the same co-channel interference, co-channel interference or unusual type of different frequency band interference, EMC design and radar system design is important .
雷达辐射源的有效分类识别是军事自动化控制和指挥系统的强烈需求。对雷达信号细微特征提取、分类和识别,不仅是现代电子对抗侦察机在复杂、密集信号环境下分选雷达信号、高可信度地识别各雷达属性的需要,同时也是雷达系统设计和抗干扰设计的需要。随着雷达技术的发展与应用,现代战场不仅表现在雷达数量的日益增多和电磁环境的日益错综复杂,还体现在电磁频谱的日益拥挤和雷达间出现的同频干扰与带内非同频干扰现象的日益严重。分析和识别低截获概率雷达信号之间的同频/非同频干扰特征,不仅有助于改进雷达体制,提高雷达抗同频干扰能力,而且也有助于雷达区分不同的干扰信号,便于采取不同的抗干扰措施和手段,抑制和保护干扰信号对雷达工作的影响或造成的损害。
为此,本文针对几种典型的大时宽低截获概率雷达信号,研究多同型雷达间同频干扰信号的特征提取与识别问题,并进行了仿真实验,主要包括以下几个方面:
(1)介绍了脉冲压缩的原理以及雷达采用脉冲压缩体制的优缺点。从脉冲压缩原理和截获概率因子入手,研究与模拟仿真了三种典型的大时宽低截获概率雷达信号:LFM、相位编码以及相参脉冲,分别给出了它们的时频特征、模糊函数和低截获概率特性,得出了低截获概率雷达信号具有低峰值功率和频带较宽这两大特点,说明了低截获概率雷达用于目前及未来舰艇及武器装备中的优越性。
(2)分别应用WVD时频分布、分数阶傅里叶变换和分形理论等方法,研究了提取表征LFM、相位编码和相参脉冲这几种大时宽低截获概率雷达信号区别的参数特征和信号特征,得出了应用这些特征的精细分析与识别,可建立雷达信号识别数据库的有效和可行性,为研究现代雷达复杂信号的分选识别和雷达抗干扰措施的运用提供了可靠保证。
(3)针对现代雷达体制下复杂信号的低截获特性,为了提高雷达辐射源信号的个体识别率,提出了一种新的分类识别方法。该方法用小波包变换提取能反映信号脉冲无意调制特征的信号各频带能量,通过泛化能力和学习能力都很强的混合核函数支持向量机进行分类识别,仿真结果证明:该方法是有效和可行的,性能优于已有方法。
(4)针对三种常见低截获概率雷达信号的干扰类型识别问题,结合分形和分数阶傅里叶变换算法,提取干扰后混合信号的信息维数、盒维数以及分数阶域信号分量能量之比,构造特征向量,然后通过支持向量机、灰色关联聚类、以及K-均值聚类积累法对干扰类型分类识别。仿真结果表明:这种方法可以对干扰类型有效分类识别,判断出干扰类型属于同型同频干扰、非同型同频干扰或是带内非同频干扰,对雷达抗干扰设计和系统设计有重要意义。
Large width of the low probability of intercept radar signals which have low power, wide bandwidth and good noise immunity and concealment are hard to be detected and identified, so they have been widely used in radar, communications and other fields. Commonly used in large pulse width are: linear frequency modulation, phase encoding and phase coherent pulse signals.Currently, the study of common radar emitter signal recognition is popular, while the research of classification and identification of LPI radar signals are not many. LPI radar has low power and wide bandwidth, so it can not easily be intercepted and identified by enemy radars, in addition, it has anti-electronic reconnaissance, anti-radiation missiles and anti-jamming techniques and has been widely used in radars, communications and other fields. It gradually becomes an important technology system and working mode in modern radar equipments. Therefore, to study the problem of anti-co-channel interference about LPI radar with large time-width and high duty cycle is of great practical significance and military reference in improving combat capability, maintaining information superiority and increasing battlefield, etc.
Therefore, this article is large for some typical low probability of intercept radar signals wide, research and more of the same type of radar signal interference between the same frequency feature extraction and recognition problems, and simulation experiments, including the following:
(1) Introduced the principle of pulse compression, and the advantages and disadvantages of using radar pulse compression system. From the pulse compression principle and probability of intercept factor to start, research and simulation of the large width of three typical low probability of intercept radar signals: LFM, and coherent phase encoding pulse, respectively, when given their frequency characteristics, fuzzy function and low probability of intercept characteristics, obtained a low probability of intercept radar signals with low peak power and wide band characteristics of these two, indicating a low probability of intercept radar for current and future ships and weapons and equipment in the superiority.
(2) Application of WVD time-frequency distribution, respectively, fractional Fourier transform and fractal theory method to study the extraction characterization LFM, phase encoding and phase coherent pulse width of these types of large, low probability of intercept radar signal characteristics of different parameters and signal characteristics, application of these characteristics obtained detailed analysis and identification of a radar signal recognition can be effective and feasibility of the database for the study of modern radar signal sorting complex identification and the use of radar anti-jamming measures provide a reliable guarantee.
(3) Under the radar system for low interception characteristics of complex signals, in order to improve the individual radar emitter signal recognition rate, a new classification method. This method can reflect the wavelet packet transform to extract the signal modulation characteristics of the signal pulse has no intention of the band energy, through the generalization and learning ability are strong mixtures of kernels support vector machines for classification, simulation results show: the method is effective and possible, performance is better than existing methods.
(4) Low probability of intercept for the three common disturbance radar signal recognition, combined with fractal and fractional Fourier transform algorithm, mixed-signal disturbance extracted information dimension, box dimension and the fractional ratio of the energy domain signal components, structure feature vector, and then support vector machine, the gray relational clustering, and the accumulation of K-means clustering method for disturbance classification. Simulation results show that: This method can effectively classify the type of interference, determine the type of disturbance of the same co-channel interference, co-channel interference or unusual type of different frequency band interference, EMC design and radar system design is important .
引文
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