脉冲信号分选技术研究

发布时间：2018-04-22 20:04

本文选题：脉冲信号 + 分选　；参考：《电子科技大学》2014年硕士论文

【摘要】：脉冲信号分选的任务就是将侦察系统接收到的脉冲信号中属于同一辐射源的脉冲分离,以备对辐射源进行识别和进一步信号处理。对脉冲信号进行有效而快速的分选,是电子侦察系统中的关键一环,各国研究人员也非常重视脉冲分选技术的研究,并取得了一定的成果。传统的脉冲信号分选以脉间参数即脉冲描述字PDW作为分选特征,在固定容差下分组作为预处理以稀释脉冲密度,再利用脉冲到达时间(TOA)参数对脉冲串的脉冲重复间隔(PRI)进行估计并去交错。传统分选方法计算简单,易于实现,能对常规体制的信号进行有效的分选,在实际工程应用中被广泛采用。随着当前信号环境复杂化,脉间参数多变,参数空间交叠严重,传统分选方法对于截获的非合作复杂脉冲信号的分选效果恶化,迫切需要分析和利用脉冲更多的信息进行分选。本文主要对非合作复杂脉冲信号分选技术进行研究和实践,主要内容如下:1.在分析当前复杂信号类型的基础上,总结了传统脉冲分选系统结构的不足,并建立了适用于复杂信号环境的改进分选结构框架,并结合新的分选框架展开对脉间参数分选、脉内特征提取和聚类分选三个方面的技术研究。2.对三种经典的脉间参数分选方法——CDIF、SDIF与PRI变换法进行了深入分析,仿真结果表明脉间参数分选方法能实现对固定PRI的脉冲信号的分选。本文在此基础上改进了PRI变换法以适应PRI抖动信号分选,并验证改进后方法对复杂信号环境下的脉冲分选取得了良好效果。3.从脉内有意调制特征入手,通过进行小波变换和模糊函数分析提取可分性好的脉内特征用于分选。对基于相位信息的小波脊提取迭代算法进行改进,并提出模糊函数特征的改进快速提取方法,提高了特征提取的速度并保留了良好的抗噪性能,经仿真验证了小波脊提取信号瞬时频率特征和模糊函数特征能有效实现不同脉内调制方式的脉冲信号分选。4.对聚类分选方法中的改进FCM算法进行研究,比较其与传统C均值聚类算法的优势。在分析总结传统C均值聚类与FCM算法的基础上,引入SAKM算法的分析,通过仿真试验表明SAKM算法实时性更强、适应性更好,更适合对未知辐射源信号的分选,有较大的发展空间。
[Abstract]:The task of pulse signal sorting is to separate the pulse which belongs to the same emitter in the pulse signal received by the reconnaissance system for identification and further signal processing of the emitter. The effective and fast sorting of pulse signals is a key link in electronic reconnaissance system. Researchers from all over the world also attach great importance to the research of pulse sorting technology and have achieved certain results. In the traditional pulse signal sorting, the pulse description word PDW is used as the sorting feature, and the pulse density is diluted by preprocessing under the fixed tolerance. Then the pulse repetition interval (PRI) of the pulse train is estimated and deinterleaved with the pulse arrival time (toa) parameter. The traditional sorting method is simple in calculation and easy to realize. It can effectively separate signals from conventional systems and is widely used in practical engineering applications. With the complexity of the current signal environment, the variable parameters between the pulse and the serious overlap of the parameters, the traditional sorting method for the intercepted non-cooperative complex pulse signal is deteriorating, so it is urgent to analyze and use more information of the pulse for sorting. This paper focuses on the research and practice of noncooperative complex pulse signal sorting technology, the main contents are as follows: 1. Based on the analysis of the current complex signal types, the shortcomings of the traditional pulse sorting system structure are summarized, and the improved sorting structure framework suitable for the complex signal environment is established, and the inter-pulse parameter sorting is carried out in combination with the new separation framework. Research on Intra-pulse feature extraction and clustering and sorting. Three classical inter-pulse parameter sorting methods, CDIF-SDIF and PRI transform, are deeply analyzed. The simulation results show that the inter-pulse parameter sorting method can realize the separation of pulse signals of fixed PRI. In this paper, the PRI transform method is improved to adapt to the PRI jitter signal sorting, and it is verified that the improved method has a good effect on pulse sorting in complex signal environment. Based on the feature of intrapulse intentional modulation, wavelet transform and ambiguity function analysis are used to extract good divisibility features for sorting. The iterative algorithm of wavelet ridge extraction based on phase information is improved, and an improved and fast method for feature extraction of fuzzy function is proposed, which improves the speed of feature extraction and retains good anti-noise performance. The simulation results show that the wavelet ridges extract the instantaneous frequency feature and the ambiguity function feature can effectively realize the pulse signal sorting with different pulse modulation modes. 4. This paper studies the improved FCM algorithm in clustering and sorting method, and compares its advantages with the traditional C-means clustering algorithm. On the basis of analyzing and summarizing the traditional C-means clustering and FCM algorithm, the analysis of SAKM algorithm is introduced. The simulation results show that the SAKM algorithm is more real-time, more adaptable, more suitable for the sorting of unknown emitter signals, and has a large development space.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN957.51

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