复杂体制雷达辐射源信号时频原子特征研究
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摘要
雷达辐射源信号特征研究是电子情报侦查(ELINT)、电子支援侦查(ESM]和雷达威胁告警(RWR)系统中的关键环节。作为辐射源信号分选和识别的前提和基础,特征分析水平直接决定了电子侦查设备性能的发挥并影响后续的作战决策。随着现代电子战的激烈对抗,新型复杂体制雷达不断投入使用并逐渐占据主导地位,电子对抗信号环境变得异常密集、复杂和多变,造成原有的信号规律被极大的破坏,导致基于载波频率(RF)、到达时间(TOA)、脉冲宽度(PW)、脉冲幅度(PA)以及到达角(DOA)的常规五参数特征提取方法已远不能适应现代电子对抗的需要。雷达辐射源信号新特征的提取,尤其是针对复杂体制雷达信号的特征提取面临着前所未有的挑战,已经成为电子对抗领域必须解决的关键问题。我国的电子对抗研究专家们在长期的理论研究和工程实践中深深感到,理论研究水平的滞后已严重制约了我国电子对抗装备技术的进一步提高。只有加强复杂体制雷达辐射源信号本质特征的研究,探索和补充新的有效特征参数,才能从根本上提高现有的电子侦查技术水平。为此,本文针对复杂体制雷达辐射源信号,提出一种辐射源信号时频原子特征分析方法,以一种全新的思路对辐射源信号的脉内特征展开了深入的研究,获得了如下的研究成果:
1.提出一种基于Gabor原子的辐射源信号特征提取方法。首先构建了适宜辐射源信号分解的过完备Gabor原子库,并引入量子遗传算法(QGA),提出基于改进QGA的原子分解快速算法解决匹配追踪(MP)过程计算量过大难以实现的问题。在此基础上,对五类典型雷达辐射源信号进行时频原子分解,得到表示信号特征信息的最匹配Gabor原子,并深入分析了不同调制类型辐射源信号Gabor原子特征参数之间的差异。最后,通过对比实验对原子特征性能进行了研究。实验结果表明,仅需要少量的Gabor即可以表示出原先信号的主要特征信息,所提取的Gabor原子特征不仅具有良好的噪声抑制能力,且可以得到不含有交叉项干扰的时频分布。
2.针对雷达辐射源信号本身固有的属性,构建了时域和频域聚集性能更加优越的过完备Chirplet原子库,并提出基于该库的辐射源信号特征提取方法。同时,引入更多生物基因的进化概念,提出一种新DNA进化算法(NDEA)。多个典型测试函数对比实验结果表明该算法具有收敛速度快、迭代次数少和全局搜索能力强的特点。在此基础上,提出基于NDEA的MP快速算法进一步降低最佳原子搜索的计算复杂度。得到表示典型辐射源信号特征信息的最匹配Chirplet原子,并对比分析Gabor原子和Chirplet原子库的性能差异。实验结果表明基于Chirplet原子的辐射源信号特征分析仅需要更少量的原子可以得到比Gabor原子更准确的特征信息,且对于分析一类时频域都重叠的多分量辐射源信号同样有效。
3.为了更有效的度量不同辐射源信号频谱之间的差异,提出了基于Spectrum原子的辐射源信号特征提取方法。首先总结了当前主要的几种频谱特征提取方法的优缺点,在对典型辐射源信号频谱特征分析的基础上,设计可以反映信号局部频谱结构的Spectrum原子,并提出多尺度Spectrum原子库构建方法。同时,采用基于库结构优化和FFT的原子分解快速算法提取出各类典型辐射源信号最匹配Spectrum原子。实验结果表明所提取的Spectrum原子参数具有一定的物理意义,不仅可以反映出不同调制类型辐射源信号频谱之间的差异,而且,对同一调制类型不同调制参数的辐射源信号,同样可以对其进行有效区分。
4.深入研究了有噪声情况下雷达辐射源信号的时频原子特征性能。提出了Chirplet原子派生特征提取算法,并设计了基于层次决策的信号分类方法对Chirplet原子特征的分类性能和抗噪性能进行了分析。同时,提出Spectrum原子特征向量构建方法,并分别采用层次决策和核聚类两种方法实现了信号的自动分类和参数估计。实验结果表明,所提取的原子特征类内聚集性强,类间分离度大,能较好的描述各类信号的分类信息,并具有良好的噪声抑制能力,进一步验证了时频原子分析方法对于复杂体制雷达辐射源信号脉内特征提取的有效性和可行性。
The feature extraction of radar emitter signals is a critical process for ELectronic INTelligence (ELINT), Electronic Support Measures (ESM) and Radar Warning Receiver (RWR) systems. As the precondition and foundation of deinterleaving and recognizing radar emitter signals, the feature extraction technique would determine the performance of the electronic reconnaissance equipment directly and influence the war strategy subsequently. Along with the countermeasure activities in modern electronic warfare are becoming more and more drastic, the advanced modern radars with complicated systems are playing a major role in the field. Due to the high-density, complex and variable electromagnetism signals environment which destroyed the signal original rules, the normal five-parameter feature extraction method, radio frequency (RF), time of arrival (TOA), pulse width (PW), pulse amplitude (PA) and direction of arrival (DOA) cannot meet the requirements of modern electronic warfare. An advanced method of feature extraction of radar emitter signals, especially for which with complex systems is a new challenge for electronic warfare. The low level of theoretic research about radar emitter signal in our country has seriously restricted the further development of modern electronic equipment. The only way to improve present technique is to enhance nature feature study of radar emitter signals, investigate new valid feature parameters.
Aiming at the key theoretical issues in signal processing of electronic warfare, A novel feature analysis method based on the time-frequency atom for radar emitter signals is presented in this dissertation. Theoretical fruits are as follows:
1 A feature extraction method of radar emitter signals based on Gabor atom is proposed. Firstly, an over-completed Gabor atom dictionary suitable for decomposing radar emitter signals is built and an improved quantum genetic algorithm (IQGA) is introduced to effectively reduce the time complexity at each search step of matching pursuit (MP), and thus the radar emitter signals are decomposed into a linear expansion of optimal Gabor atoms representing the signal feature. Then, the dissertation studies the Gabor atom feature parameter difference among radar emitter signals with various modulation types, and analyses the Gabor atom feature performance by comparing the difference. The experiment result shows that even a small number Gabor atoms can express the major feature of original signals. The extracted Gabor atom feature not only has good noise-suppression ability, but also has the non-cross interference time-frequency distribution.
2 Based on the over-completed Chirplet atom dictionary with superior gathering ability in time-frequency domains, a feature extraction method for radar emitter signal is presented. At the same time, a novel DNA evolution algorithm (NDEA) is proposed. Comparison of DNEA with other algorithms for typical complex functions demonstrates the algorithm has good characteristics of of rapid convergence, short computing times and strong search capability. Then, the fast MP algorithm based on NDEA is applied to effectively reduce the complexity of searching calculations, and thus some best-matched Chirplet atoms representing features of typical radar emitter signals are obtained. Furthermore, the dissertation compares and analyses the performance difference between Gabor and Chirplet atom dictionaries. The experiment results show that the smaller number Chirplet atom can represent more accurate feature of radar emitter signals compared to Gabor atom.
3 In order to effectively measure the spectrum difference among radar emitter signals, a feature extraction method based on Spectrum atom is presented. At the beginning, the advantages and disadvantages of current main spectrum feature extraction methods are concluded. Based on it, the Spectrum atom expressing the local spectrum structure of signals is designed and a method of multi-scale Spectrum atom dictionary is proposed. Meantime, the fast FFT algorithm is applied to extract the best-matched Spectrum atom for all kinds of traditional radar emitter signals. The experiment results show that the extracted Spectrum atom parameters have a certain physical meaning which not only can express the spectrum difference among various modulation types, but also can recognize the radar emitter signals with same modulation type and different modulation parameters.
4 The time-frequency atom feature performance of radar emitter signals on the noise condition are studied further. Firstly, the Chirplet atom derived feature extraction algorithm is presented and a signal classification method based on hierarchy strategy is designed to analyse the classification and noise-suppression ability of Chirplet atom. Then, the method to achieve the Spectrum atom characteristics vector according to the parameters of the atoms is proposed. Finally, the hierarchy strategy and the kernelized clustering algorithm are applied to realize the signal automatic classification and parameter estimate. The experiment results show that the extracted atom feature has good property of clustering the same and separating different radar emitter signals, and has good noise-suppression ability, which further confirms that the time-frequency atom analysis method is effective and feasible for feature extraction of radar emitter signals.
This work is supported by the National Natural Science Foundation of China (No. 60572143) and the National Electronic Warfare Laboratory Foundation (No. 51435QT220401).
1.提出一种基于Gabor原子的辐射源信号特征提取方法。首先构建了适宜辐射源信号分解的过完备Gabor原子库,并引入量子遗传算法(QGA),提出基于改进QGA的原子分解快速算法解决匹配追踪(MP)过程计算量过大难以实现的问题。在此基础上,对五类典型雷达辐射源信号进行时频原子分解,得到表示信号特征信息的最匹配Gabor原子,并深入分析了不同调制类型辐射源信号Gabor原子特征参数之间的差异。最后,通过对比实验对原子特征性能进行了研究。实验结果表明,仅需要少量的Gabor即可以表示出原先信号的主要特征信息,所提取的Gabor原子特征不仅具有良好的噪声抑制能力,且可以得到不含有交叉项干扰的时频分布。
2.针对雷达辐射源信号本身固有的属性,构建了时域和频域聚集性能更加优越的过完备Chirplet原子库,并提出基于该库的辐射源信号特征提取方法。同时,引入更多生物基因的进化概念,提出一种新DNA进化算法(NDEA)。多个典型测试函数对比实验结果表明该算法具有收敛速度快、迭代次数少和全局搜索能力强的特点。在此基础上,提出基于NDEA的MP快速算法进一步降低最佳原子搜索的计算复杂度。得到表示典型辐射源信号特征信息的最匹配Chirplet原子,并对比分析Gabor原子和Chirplet原子库的性能差异。实验结果表明基于Chirplet原子的辐射源信号特征分析仅需要更少量的原子可以得到比Gabor原子更准确的特征信息,且对于分析一类时频域都重叠的多分量辐射源信号同样有效。
3.为了更有效的度量不同辐射源信号频谱之间的差异,提出了基于Spectrum原子的辐射源信号特征提取方法。首先总结了当前主要的几种频谱特征提取方法的优缺点,在对典型辐射源信号频谱特征分析的基础上,设计可以反映信号局部频谱结构的Spectrum原子,并提出多尺度Spectrum原子库构建方法。同时,采用基于库结构优化和FFT的原子分解快速算法提取出各类典型辐射源信号最匹配Spectrum原子。实验结果表明所提取的Spectrum原子参数具有一定的物理意义,不仅可以反映出不同调制类型辐射源信号频谱之间的差异,而且,对同一调制类型不同调制参数的辐射源信号,同样可以对其进行有效区分。
4.深入研究了有噪声情况下雷达辐射源信号的时频原子特征性能。提出了Chirplet原子派生特征提取算法,并设计了基于层次决策的信号分类方法对Chirplet原子特征的分类性能和抗噪性能进行了分析。同时,提出Spectrum原子特征向量构建方法,并分别采用层次决策和核聚类两种方法实现了信号的自动分类和参数估计。实验结果表明,所提取的原子特征类内聚集性强,类间分离度大,能较好的描述各类信号的分类信息,并具有良好的噪声抑制能力,进一步验证了时频原子分析方法对于复杂体制雷达辐射源信号脉内特征提取的有效性和可行性。
The feature extraction of radar emitter signals is a critical process for ELectronic INTelligence (ELINT), Electronic Support Measures (ESM) and Radar Warning Receiver (RWR) systems. As the precondition and foundation of deinterleaving and recognizing radar emitter signals, the feature extraction technique would determine the performance of the electronic reconnaissance equipment directly and influence the war strategy subsequently. Along with the countermeasure activities in modern electronic warfare are becoming more and more drastic, the advanced modern radars with complicated systems are playing a major role in the field. Due to the high-density, complex and variable electromagnetism signals environment which destroyed the signal original rules, the normal five-parameter feature extraction method, radio frequency (RF), time of arrival (TOA), pulse width (PW), pulse amplitude (PA) and direction of arrival (DOA) cannot meet the requirements of modern electronic warfare. An advanced method of feature extraction of radar emitter signals, especially for which with complex systems is a new challenge for electronic warfare. The low level of theoretic research about radar emitter signal in our country has seriously restricted the further development of modern electronic equipment. The only way to improve present technique is to enhance nature feature study of radar emitter signals, investigate new valid feature parameters.
Aiming at the key theoretical issues in signal processing of electronic warfare, A novel feature analysis method based on the time-frequency atom for radar emitter signals is presented in this dissertation. Theoretical fruits are as follows:
1 A feature extraction method of radar emitter signals based on Gabor atom is proposed. Firstly, an over-completed Gabor atom dictionary suitable for decomposing radar emitter signals is built and an improved quantum genetic algorithm (IQGA) is introduced to effectively reduce the time complexity at each search step of matching pursuit (MP), and thus the radar emitter signals are decomposed into a linear expansion of optimal Gabor atoms representing the signal feature. Then, the dissertation studies the Gabor atom feature parameter difference among radar emitter signals with various modulation types, and analyses the Gabor atom feature performance by comparing the difference. The experiment result shows that even a small number Gabor atoms can express the major feature of original signals. The extracted Gabor atom feature not only has good noise-suppression ability, but also has the non-cross interference time-frequency distribution.
2 Based on the over-completed Chirplet atom dictionary with superior gathering ability in time-frequency domains, a feature extraction method for radar emitter signal is presented. At the same time, a novel DNA evolution algorithm (NDEA) is proposed. Comparison of DNEA with other algorithms for typical complex functions demonstrates the algorithm has good characteristics of of rapid convergence, short computing times and strong search capability. Then, the fast MP algorithm based on NDEA is applied to effectively reduce the complexity of searching calculations, and thus some best-matched Chirplet atoms representing features of typical radar emitter signals are obtained. Furthermore, the dissertation compares and analyses the performance difference between Gabor and Chirplet atom dictionaries. The experiment results show that the smaller number Chirplet atom can represent more accurate feature of radar emitter signals compared to Gabor atom.
3 In order to effectively measure the spectrum difference among radar emitter signals, a feature extraction method based on Spectrum atom is presented. At the beginning, the advantages and disadvantages of current main spectrum feature extraction methods are concluded. Based on it, the Spectrum atom expressing the local spectrum structure of signals is designed and a method of multi-scale Spectrum atom dictionary is proposed. Meantime, the fast FFT algorithm is applied to extract the best-matched Spectrum atom for all kinds of traditional radar emitter signals. The experiment results show that the extracted Spectrum atom parameters have a certain physical meaning which not only can express the spectrum difference among various modulation types, but also can recognize the radar emitter signals with same modulation type and different modulation parameters.
4 The time-frequency atom feature performance of radar emitter signals on the noise condition are studied further. Firstly, the Chirplet atom derived feature extraction algorithm is presented and a signal classification method based on hierarchy strategy is designed to analyse the classification and noise-suppression ability of Chirplet atom. Then, the method to achieve the Spectrum atom characteristics vector according to the parameters of the atoms is proposed. Finally, the hierarchy strategy and the kernelized clustering algorithm are applied to realize the signal automatic classification and parameter estimate. The experiment results show that the extracted atom feature has good property of clustering the same and separating different radar emitter signals, and has good noise-suppression ability, which further confirms that the time-frequency atom analysis method is effective and feasible for feature extraction of radar emitter signals.
This work is supported by the National Natural Science Foundation of China (No. 60572143) and the National Electronic Warfare Laboratory Foundation (No. 51435QT220401).
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