基于脉内特征的雷达辐射源信号分选技术研究
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摘要
雷达辐射源信号分选是将截获的交错脉冲信号进行分组的过程,使得同组中的脉冲信号来自于同一部雷达辐射源。它是雷达电子支援侦察(ESM)和电子情报侦察(ELINT)应用中的重要组成部分,直接影响着电子侦察设备性能的发挥并关系到战争的后续作战决策。目前传统的分选方法主要采用到达时间(TOA),载波频率(RF),脉冲宽度(PW),脉冲幅度(PA)以及到达方向(DOA)五个参数实现脉冲列的去交错。这在只有常规体制雷达且辐射源数量不多的情况下确可取得满意的效果。但随着现代电子对抗的日益激烈,电磁环境的信号密度日趋密集和复杂新体制雷达不断出现,从而造成了脉冲大量的丢失以及信号参数空间的严重交叠,破坏了分选所利用的信号规律性,导致了基于脉间五参数的分选算法已难以获得令人满意的分选效果。近年来,随着数字信号处理技术(DSP)和大规模集成电路(VLSI)的发展,数字中频接收机能够获取全部的雷达特征信息,利用脉内特征参数进行脉冲去交错处理是另外一种有望提高分选性能的方法。为此,本文针对截获的未知雷达脉冲信号,在传统分选性能急剧下降,甚至完全失效的情形下,从雷达辐射源信号的有意和无意调制特征提取与性能分析、雷达辐射源信号的分类数目估计和聚类分选算法三个方面对基于脉内特征的雷达辐射源信号分选相关理论问题展开讨论和研究,主要工作和研究成果如下：
1.为研究复杂雷达辐射源信号脉内调制特性,提出符号化时间序列特征、频谱小波变换特征和差分自相关包络特征提取算法,以便提取和补充新的,适合于工程应用的有效分选特征向量(DFV)。
雷达辐射源信号在频域分布紧凑,不同调制信号的频谱明显易分辨。针对这一特点,提出一种对雷达辐射源信号频谱进行符号化分析的脉内特征提取方法,该方法能够快速有效的提取定量信息。将符号化过程中反映信号自相关特征的采样时延和反映信号调制规律特性的符号熵作为脉内特征。通过对7种雷达信号的仿真实验和数据分析表明,所提取的特征具有良好的抗噪性和可聚类性。同时算法计算简单快捷,能够简化分类器设计。
雷达辐射源信号在传播和处理过程中易受到噪声干扰,信噪比变化很大。本文在小波域滤波算法的基础上提出一种对雷达辐射源信号进行脉内特征提取方法,该方法能够从信号中有效的提取定量信息。将小波变换后低频逼近小波系数的能量分布熵与经过尺度相关去噪计算后反映信号边缘的高频细节小波系数能量分布熵构成雷达辐射源信号的二维特征向量。通过对10种雷达辐射源信号的特征提取和分类仿真实验分析表明：提取的样本特征在0dB下具有很好的抗噪性和可聚类性,证实了本文方法的有效性。
在统计自相关函数的基础上提出一种对雷达辐射源信号进行脉内特征提取的方法。该方法首先利用一阶差分来突出信号的调制特征；然后将差分的结果通过自相关计算来抑制噪声的影响,提取不同时延下自相关函数的包络特征；最后,根据提出的基于距离的可分性判据对包络进行特征选择,得到最能表征信号调制特征的二维或三维特征向量。通过对6种典型辐射源信号的特征提取和分类仿真实验分析表明：提取的特征在低信噪比(-5～0)下仍具有较好的抗噪性和可聚类性。
2.特定辐射源识别(SEI)技术研究立足于从截获的辐射源信号中提取细微且稳健的特征,这些特征是由特定辐射源个体所决定的指纹信息。雷达辐射源信号因无法避免的雷达发射机相位噪声影响而具有无意调制个体特征。文中采用围线积分双谱提取由振荡器相位噪声所造成的无意调制个体特征,并将围线积分双谱的均值、波形熵和双谱熵作为量化特征衡量不同雷达辐射源之间的个体差异。仿真实验表明,提取的量化特征在一定的信噪比环境下较好地体现辐射源之间的个体差异性,并且能够实现辐射源个体的分类识别。
由于脉冲包络的形状变化信息能够提供不同源的个体特征并有助于信号的分选识别,文中提出一种基于主成分分析的辐射源个数估计算法。这种方法通过对接收到的脉冲信号包络进行主成分分析提取其相关矩阵的特征值,利用特征值构造的信息论准则估计雷达辐射源信号的个数。最后通过计算机仿真实验和对比已有的其它信息论准则验证了本文方法的可行性和有效性
3.针对雷达辐射源信号特征分布形式复杂、类边界归并突出的问题,提出一种基于灰关联测度的聚类分选模型,并对其中涉及的各种相关理论问题展开详细而专门的讨论。
在该模型中,提出了一种基于灰关联测度的分裂式层次聚类算法。该算法利用灰关联测度来衡量数据对象之间的相似程度,采用自顶向下基于密度扩展的分裂式层次化聚类策略,生成不同层次的数据集划分,然后根据提出的聚类有效性指标衡量不同聚类划分的质量；将有效性指标曲线极值点所对应的聚类划分用于估计最佳聚类数目。在实际数据和人工合成数据集上的实验表明,该算法能够获得较好的聚类结果,并且能够识别任意形状的簇。
考虑辐射源数量、截获的脉冲数量、脉冲丢失率以及环境信噪比等多种因素,论文研究了基于灰关联测度的聚类分选模型在提取的脉内特征以及不同特征组合数据集上的分选性能。实验结果表明聚类分选算法对复杂体制雷达辐射源信号具有良好的分选效果,且对不同因素的影响表现较好的稳健性,为探索有效特征向量提供了参考依据。
Radar signal deinterleaving is to separate the pulse trains interleaved in time into individual emitter groups, where pulses in one group belong to the same emitter. It is basic and important function in the electronic intelligence system and electronic support measure systems and it directly determines performance of electronic reconnaissance equipement. The aim of signal deinterleaving is to provide corresponding pulse sequence to signal analysis, measurement and recognition. Nowadays conventional methods of signal deinterleaving use each pulse signal parameters, i.e., time of arrival (TOA), radio frequency (RF), pulse width (PW), pulse amplitude (PA) and direction of arrival (DOA). This technique works well when the signal is received in low noise, and the signal belongs to continuous wave radar emitter. However, as the countermeasure activities in modern electronic warfare are becoming more and more drastic, the density of electromagnetism signals environment arrives at the degree of mega and the application of new complex waveform modulations in modern radar, which has the ability to result in pulse loss, parameter overlapping heavily and destroy the regularities of signal sorting. Thus, the conventional inter-pulse techniques may not to be enough to distinguish them one from another in such environment. In recent years, with the development of modern digital signal processing technology and VLSI, the radar medium frequency receiver can obtain radar signals digital waveform. This seems to be enough to put them into practice using intra-pulse feature to achieve high-accuracy sorting radar emitters in high density and high interleaving environment. In this dissertation, we focus on the problems of deinterleaving based on unknown radar pulse signals and lucubrate from three facts when the classical pulse sorting methods are badly destroyed, i.e., the intentional and unintentional modulation feature extraction and performance analysis for radar signals, the cluster number estimation of radar emitter signals and the novel intrapulse-based sorting approach related to cluster algorithm. The main research results are described as follows:
1. In order to research on intentional modulation characteristics of advanced radar emitter signals, the feature extraction algorithms based on symbolic time series analysis(STSA), wavelet transform in spectrum and autocorrelation function of first difference are proposed. These algorithms can provide new parameter vectors which are advantage to engineering application.
Because the waveform of radar emitter signals have compact distribution and good shape in frequency domain, an approach for intra-pulse feature extraction of radar emitter signals based on STSA is proposed. It is efficient to obtain quantitative information from signals. Embedding time-delay and modified Shannon entropy are used as two-dimensional feature vector to sort the interleaving radar signals. The time-delay feature can determine the length of symbol series. The entropy feature can quantitatively reveal deterministic information and complexity of radar intra-pulse modulation signals. Experimental result and data analysis indicate that the features of seven typical radar emitter signals extracted by STSA have good characteristics of clustering and suppressing noise. Moreover, the algorithm is of advantage to engineering application and implementation because of its computational simpleness, efficiency and capability of simplifying sorter.
In fact, radar emitter signals are always interfered with by plenty of noise in the process of transmission in the air and in the process of receiving and processing in scout. This paper presents an approach for intra-pulse feature extraction of radar emitter signals based on wavelet transform domain filtering. It is efficient to obtain quantitative information from signals. The energy entropy from approximation coefficients of wavelet transform and the other energy entropy from inner-scale correlations denoise of detail coefficients are used as two-dimensional feature vector. Experiment results demonstrate that the features of ten typical radar emitter signals extracted by wavelet transform have good performance of noise-resistance and clustering when SNR is OdB.
An approach for intra-pulse feature extraction of radar emitter signals based on the first difference autocorrelation function is proposed. The envelop feature extraction of autocorrelation function involves the use of first difference operation of radar emitter signals, which can enhance the difference in modulation information and has good noise abatement. The criterion, defined as degree of separability, is used as two-dimensional and three-dimensional feature vectors selection. Experiment results demonstrate that the features of six typical radar emitter signals extracted by autocorrelation function have good performance of noise-resistance and clustering when SNR varies from-5dB to OdB.
2. Specific emitter identification(SEI) technology extracts subtle and persistent features from received pulse signal to create a fingerprint unique to specific radar. Radar emitter signals have their individual feature due to inevitable transmitter phase noise. An approach based on surrounding-line integrated bispectrum is proposed to extract unintentional phase modulation features caused by oscillator. The quantitative features, i.e. bispectra entropy, mean and waveform entropy of surrounding-line integrated bispectrum, is further extracted from bispectrum to reveal the individual difference between emitters. Computer simulation results show that the quantitative features can classify emitters using individual difference under moderate SNR.
Examining the unintentional shape variations in pulse envelope can provide insight into the transmitter type and may help to identify the signal. In this paper, the approach for estimating the number of emitters is proposed based on model selection of eigenvalues from principal component analysis(PCA) of pulse envelope vectors. A novel information theoretic criterion is formulated for determining the number of emitters. When compared with the other information theoretic criterions, computer simulations show that the effectiveness and feasibility of the proposed approach.
3. Aiming at the issue of complicated characteristics distribution and undistinguishable boundary between clusters of radar signals, a deinterleaving models using clustering algorithm based on grey relational analysis is proposed. And the theoretical problems relative to these models are discussed in detail.
A divisive hierarchical clustering algorithm based on grey relational measure is proposed. In the algorithm, grey relational analysis is used to measure the degree of similarity between data set. On the basis of generation of top-down density-based hierarchical partitions of data set and proposed clustering validity index, the extremum of the index curve is used to estimate the number of clusters. Computer simulation results on Benchmark data set and synthesis data set demonstrate that the proposed algorithm is feasible and has good clustering performance especially for arbitrary shaped clusters.
In consideration of some factors, such as the number of radar emitters, the amount of intercepted pules, pulse loss rate and signal-noise-ratio etc, the performance of deinterleaving algorithm based on clustering are studied in deep. The experimental result on intra-pulse features vectors of radar signals and different combinations of features vectors show that the clustering algorithm based on grey relational measure has good performance in sorting radar emitter signals and has good stability under the above factors. Additionally, the experiment gives some useful conclusion and reference in chosing new effective deinterleaving feature vector.
1.为研究复杂雷达辐射源信号脉内调制特性,提出符号化时间序列特征、频谱小波变换特征和差分自相关包络特征提取算法,以便提取和补充新的,适合于工程应用的有效分选特征向量(DFV)。
雷达辐射源信号在频域分布紧凑,不同调制信号的频谱明显易分辨。针对这一特点,提出一种对雷达辐射源信号频谱进行符号化分析的脉内特征提取方法,该方法能够快速有效的提取定量信息。将符号化过程中反映信号自相关特征的采样时延和反映信号调制规律特性的符号熵作为脉内特征。通过对7种雷达信号的仿真实验和数据分析表明,所提取的特征具有良好的抗噪性和可聚类性。同时算法计算简单快捷,能够简化分类器设计。
雷达辐射源信号在传播和处理过程中易受到噪声干扰,信噪比变化很大。本文在小波域滤波算法的基础上提出一种对雷达辐射源信号进行脉内特征提取方法,该方法能够从信号中有效的提取定量信息。将小波变换后低频逼近小波系数的能量分布熵与经过尺度相关去噪计算后反映信号边缘的高频细节小波系数能量分布熵构成雷达辐射源信号的二维特征向量。通过对10种雷达辐射源信号的特征提取和分类仿真实验分析表明：提取的样本特征在0dB下具有很好的抗噪性和可聚类性,证实了本文方法的有效性。
在统计自相关函数的基础上提出一种对雷达辐射源信号进行脉内特征提取的方法。该方法首先利用一阶差分来突出信号的调制特征；然后将差分的结果通过自相关计算来抑制噪声的影响,提取不同时延下自相关函数的包络特征；最后,根据提出的基于距离的可分性判据对包络进行特征选择,得到最能表征信号调制特征的二维或三维特征向量。通过对6种典型辐射源信号的特征提取和分类仿真实验分析表明：提取的特征在低信噪比(-5～0)下仍具有较好的抗噪性和可聚类性。
2.特定辐射源识别(SEI)技术研究立足于从截获的辐射源信号中提取细微且稳健的特征,这些特征是由特定辐射源个体所决定的指纹信息。雷达辐射源信号因无法避免的雷达发射机相位噪声影响而具有无意调制个体特征。文中采用围线积分双谱提取由振荡器相位噪声所造成的无意调制个体特征,并将围线积分双谱的均值、波形熵和双谱熵作为量化特征衡量不同雷达辐射源之间的个体差异。仿真实验表明,提取的量化特征在一定的信噪比环境下较好地体现辐射源之间的个体差异性,并且能够实现辐射源个体的分类识别。
由于脉冲包络的形状变化信息能够提供不同源的个体特征并有助于信号的分选识别,文中提出一种基于主成分分析的辐射源个数估计算法。这种方法通过对接收到的脉冲信号包络进行主成分分析提取其相关矩阵的特征值,利用特征值构造的信息论准则估计雷达辐射源信号的个数。最后通过计算机仿真实验和对比已有的其它信息论准则验证了本文方法的可行性和有效性
3.针对雷达辐射源信号特征分布形式复杂、类边界归并突出的问题,提出一种基于灰关联测度的聚类分选模型,并对其中涉及的各种相关理论问题展开详细而专门的讨论。
在该模型中,提出了一种基于灰关联测度的分裂式层次聚类算法。该算法利用灰关联测度来衡量数据对象之间的相似程度,采用自顶向下基于密度扩展的分裂式层次化聚类策略,生成不同层次的数据集划分,然后根据提出的聚类有效性指标衡量不同聚类划分的质量；将有效性指标曲线极值点所对应的聚类划分用于估计最佳聚类数目。在实际数据和人工合成数据集上的实验表明,该算法能够获得较好的聚类结果,并且能够识别任意形状的簇。
考虑辐射源数量、截获的脉冲数量、脉冲丢失率以及环境信噪比等多种因素,论文研究了基于灰关联测度的聚类分选模型在提取的脉内特征以及不同特征组合数据集上的分选性能。实验结果表明聚类分选算法对复杂体制雷达辐射源信号具有良好的分选效果,且对不同因素的影响表现较好的稳健性,为探索有效特征向量提供了参考依据。
Radar signal deinterleaving is to separate the pulse trains interleaved in time into individual emitter groups, where pulses in one group belong to the same emitter. It is basic and important function in the electronic intelligence system and electronic support measure systems and it directly determines performance of electronic reconnaissance equipement. The aim of signal deinterleaving is to provide corresponding pulse sequence to signal analysis, measurement and recognition. Nowadays conventional methods of signal deinterleaving use each pulse signal parameters, i.e., time of arrival (TOA), radio frequency (RF), pulse width (PW), pulse amplitude (PA) and direction of arrival (DOA). This technique works well when the signal is received in low noise, and the signal belongs to continuous wave radar emitter. However, as the countermeasure activities in modern electronic warfare are becoming more and more drastic, the density of electromagnetism signals environment arrives at the degree of mega and the application of new complex waveform modulations in modern radar, which has the ability to result in pulse loss, parameter overlapping heavily and destroy the regularities of signal sorting. Thus, the conventional inter-pulse techniques may not to be enough to distinguish them one from another in such environment. In recent years, with the development of modern digital signal processing technology and VLSI, the radar medium frequency receiver can obtain radar signals digital waveform. This seems to be enough to put them into practice using intra-pulse feature to achieve high-accuracy sorting radar emitters in high density and high interleaving environment. In this dissertation, we focus on the problems of deinterleaving based on unknown radar pulse signals and lucubrate from three facts when the classical pulse sorting methods are badly destroyed, i.e., the intentional and unintentional modulation feature extraction and performance analysis for radar signals, the cluster number estimation of radar emitter signals and the novel intrapulse-based sorting approach related to cluster algorithm. The main research results are described as follows:
1. In order to research on intentional modulation characteristics of advanced radar emitter signals, the feature extraction algorithms based on symbolic time series analysis(STSA), wavelet transform in spectrum and autocorrelation function of first difference are proposed. These algorithms can provide new parameter vectors which are advantage to engineering application.
Because the waveform of radar emitter signals have compact distribution and good shape in frequency domain, an approach for intra-pulse feature extraction of radar emitter signals based on STSA is proposed. It is efficient to obtain quantitative information from signals. Embedding time-delay and modified Shannon entropy are used as two-dimensional feature vector to sort the interleaving radar signals. The time-delay feature can determine the length of symbol series. The entropy feature can quantitatively reveal deterministic information and complexity of radar intra-pulse modulation signals. Experimental result and data analysis indicate that the features of seven typical radar emitter signals extracted by STSA have good characteristics of clustering and suppressing noise. Moreover, the algorithm is of advantage to engineering application and implementation because of its computational simpleness, efficiency and capability of simplifying sorter.
In fact, radar emitter signals are always interfered with by plenty of noise in the process of transmission in the air and in the process of receiving and processing in scout. This paper presents an approach for intra-pulse feature extraction of radar emitter signals based on wavelet transform domain filtering. It is efficient to obtain quantitative information from signals. The energy entropy from approximation coefficients of wavelet transform and the other energy entropy from inner-scale correlations denoise of detail coefficients are used as two-dimensional feature vector. Experiment results demonstrate that the features of ten typical radar emitter signals extracted by wavelet transform have good performance of noise-resistance and clustering when SNR is OdB.
An approach for intra-pulse feature extraction of radar emitter signals based on the first difference autocorrelation function is proposed. The envelop feature extraction of autocorrelation function involves the use of first difference operation of radar emitter signals, which can enhance the difference in modulation information and has good noise abatement. The criterion, defined as degree of separability, is used as two-dimensional and three-dimensional feature vectors selection. Experiment results demonstrate that the features of six typical radar emitter signals extracted by autocorrelation function have good performance of noise-resistance and clustering when SNR varies from-5dB to OdB.
2. Specific emitter identification(SEI) technology extracts subtle and persistent features from received pulse signal to create a fingerprint unique to specific radar. Radar emitter signals have their individual feature due to inevitable transmitter phase noise. An approach based on surrounding-line integrated bispectrum is proposed to extract unintentional phase modulation features caused by oscillator. The quantitative features, i.e. bispectra entropy, mean and waveform entropy of surrounding-line integrated bispectrum, is further extracted from bispectrum to reveal the individual difference between emitters. Computer simulation results show that the quantitative features can classify emitters using individual difference under moderate SNR.
Examining the unintentional shape variations in pulse envelope can provide insight into the transmitter type and may help to identify the signal. In this paper, the approach for estimating the number of emitters is proposed based on model selection of eigenvalues from principal component analysis(PCA) of pulse envelope vectors. A novel information theoretic criterion is formulated for determining the number of emitters. When compared with the other information theoretic criterions, computer simulations show that the effectiveness and feasibility of the proposed approach.
3. Aiming at the issue of complicated characteristics distribution and undistinguishable boundary between clusters of radar signals, a deinterleaving models using clustering algorithm based on grey relational analysis is proposed. And the theoretical problems relative to these models are discussed in detail.
A divisive hierarchical clustering algorithm based on grey relational measure is proposed. In the algorithm, grey relational analysis is used to measure the degree of similarity between data set. On the basis of generation of top-down density-based hierarchical partitions of data set and proposed clustering validity index, the extremum of the index curve is used to estimate the number of clusters. Computer simulation results on Benchmark data set and synthesis data set demonstrate that the proposed algorithm is feasible and has good clustering performance especially for arbitrary shaped clusters.
In consideration of some factors, such as the number of radar emitters, the amount of intercepted pules, pulse loss rate and signal-noise-ratio etc, the performance of deinterleaving algorithm based on clustering are studied in deep. The experimental result on intra-pulse features vectors of radar signals and different combinations of features vectors show that the clustering algorithm based on grey relational measure has good performance in sorting radar emitter signals and has good stability under the above factors. Additionally, the experiment gives some useful conclusion and reference in chosing new effective deinterleaving feature vector.
引文
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