【作者】 张葛祥；

【导师】 金炜东； 胡来招；

【作者基本信息】 西南交通大学 ， 信号与信息处理， 2005， 博士

【摘要】 雷达辐射源信号识别是电子情报侦察(ELINT)、电子支援侦察(ESM)和雷达威胁告警(RWR)系统中的关键处理过程,也是电子干扰的前提和基础,其识别水平是衡量雷达对抗设备技术先进程度的重要标志。随着现代电子战的激烈对抗,复杂体制雷达辐射源迅速增加并逐渐占居主导地位,复杂多变的信号形式大大弱化并逐渐使传统识别方法失去其有效性。雷达辐射源信号识别已面临前所未有的挑战。目前,我国识别复杂体制雷达辐射源信号的水平与美国等发达国家相比,差距十分明显。其根本原因在于我国在此方面的理论研究水平较低,缺乏支撑雷达对抗装备技术改进的理论根据。作为我国电子对抗的核心科研基地,西南电子设备研究所的专家们在现代电子装备的研究中深深感到,当前雷达对抗装备的技术水平难以适应复杂体制雷达辐射源占主导地位的电子对抗环境,雷达辐射源信号识别的现有方法滞后于迅速发展的雷达技术。因此,复杂体制雷达辐射源信号识别成为电子对抗领域中迫切需要解决的关键问题,只有探索出有效的识别新方法,才能从根本上提高ELINT、ESM和RWR系统的技术水平 近年来,尽管雷达辐射源信号识别受到电子对抗人员的高度重视,并提出了多种识别方法,但是,采用常规五参数的传统识别方法及其改进方法在识别新体制雷达辐射源信号时遇到严重困难,而现有的脉内细微特征分析法主要对少数两三种雷达辐射源信号进行定性分析,且很少考虑噪声的影响,难以满足现代信息战对电子对抗侦察系统智能化的要求。对于识别复杂多变的新体制雷达辐射源信号的难题,首先需要从不同角度采用多种方法探索新体制复杂辐射源信号的有效特征,然后对高维特征进行筛选、降维,再用高效的分类器实现自动分类识别。通过系统的理论研究,形成能对雷达辐射源信号识别的技术改进予以有效支持的理论方法体系。 针对我国雷达对抗信号处理研究工作中迫切需要解决的关键理论问题,本文对雷达辐射源信号智能识别的模型结构和算法进行了探索性和系统性研究,获得了如下的研究成果: 1 提出一种新的雷达辐射源信号识别模型结构,以一种崭新的思路研更多还原

【Abstract】 Radar emitter signal recognition is one of the key procedure of signal processing in ELectronic INTelligence (ELINT), Electronic Support Measures (ESM) and Radar Warning Receiver (RWR) systems in electronic warfare. It is also the precondition and foundation of electronic interfering. The state of the art of radar emitter signal recognition corresponds to the technical merit of electronic reconnaissance equipment. As countermeasure activities in modern electronic warfare become more and more drastic, advanced radars increase rapidly and become the main component of radars gradually. Complex and changeful signal waveform weakens greatly the validity of traditional recognition methods and makes the validity lose gradually. Radar emitter signal recognition has been confronted with strange challenges. At present, there is a big gap between the state of the art of recognizing advanced radar emitter signals in our country and that in some advanced countries such as the United States of America. The prime reason lies in low level of theoretic research about radar emitter signal recognition in our country and lack of theoretic basis for improving counter-radar equipments. Southwest Institute of Electronic Equipment (SWIEE) is a key base of scientific research in electronic warfare in our country. In the process of studying modern electronic equipment, the experts of SWIEE profoundly point out that the currently technical merit of counter-radar equipments cannot adapt the complex environment of modern electronic warfare in which advanced radars will have a decisive influence, and the existing methods for recognizing radar emitter signals lag behind rapid-development radar technique. Therefore, the recognition of advanced radar emitter signals becomes a key problem to solve urgently in electronic warfare. Only some new and valid approaches are explored, can the technical merit of ELINT, ESM and RWR systems be enhanced radically.In recent years, although radar emitter signal recognition is paid much attention and some recognition methods were presented, using conventional 5 parameters, traditional recognition methods and their improved methodsencounter serious difficulties in identifying advanced radar emitter signals. Unfortunately, the existing intrapulse characteristic extraction approaches only analyze qualitatively two or three radar emitter signals without considering the effects of noise nearly. So the approaches cannot meet the intelligentized requirements of modern information warfare for electronic warfare reconnaissance systems. For the difficult problem of recognizing complicatedly and changefully advanced radar emitter signals, multiple methods need be used to extract the valid features from radar emitter signals from multiple different views firstly. The features construct a high-dimensional feature vector. Then, some feature selection methods are proposed to select the most discriminatory features and to eliminate redundant features so as to lower the dimensionality of the feature vector. Finally, efficient classifiers are designed to fulfill automatic recognition of radar emitter signals. Through theoretic research systematically, the theory can be established to improving the techniques for recognizing radar emitter signals effectively.Aiming at the key issue to solve urgently in signal processing of electronic warfare in our country, intelligent recognition model and algorithms for advanced radar emitter signals are studied systematically and exploringly in this dissertation. Theoretical fruits are as follows.1 A new model structure for recognizing radar emitter signals is proposed and consequently a fire-new thinking is introduced to solve the difficult problem of advanced radar emitter signal recognition. The existing matching technique using 5 conventional parameters identifies advanced radar emitter signals difficultly. According to the characteristics of advanced radar emitter signals and the drawbacks of the existing methods, this dissertation uses a new way, including feature analysis and extraction, feature evaluation and selection, classifier design, to solve the difficult issue of radar emitter signal recognition. The algorithms for implementing the proposed model structure and testing experiments validate that the new model structure is more effective than the current model structure.2 When Signal-to-Noise Rate (SNR) varies in a certain range, Severalfeatures of radar emitter signals are analyzed and studied quantificationally. From different views, different feature extraction methods are used to explore the intrinsical characteristics. Several methods including resemblance coefficient feature extraction, entropy feature extraction, complexity feature extraction and wavelet packet transform feature extraction are proposed to extract features from radar emitter signals. Noise-suppression performances of resemblance coefficient features, entropy features and complexity features are analyzed qualitatively or quantificationally. Experimental results show that these features are not nearly affected by noise and have good stability when SNR is above 5 dB or 6 dB. Using the several feature extraction methods, two resemblance coefficient features, two entropy features (approximate entropy and norm entropy), 8 wavelet packet decomposition features and 4 complexity features (Lempel-Ziv complexity, information dimension, box dimension and correlation dimension) are extracted from multiple radar emitter signals. Furthermore, the time complexities of the feature extraction methods, the within-class clustering and between-class separability of these features are used to evaluate comparatively the performances of the methods and the qualities of the features.3 After the features are extracted adequately from radar emitter signals, models and algorithms for exploring intrinsical features of radar emitter signals are studied further. Five feature selection methods, including class-separability, satisfactory rate of feature set, resemblance coefficient, rough set theory and discriminability coefficient, are proposed to analyze and evaluate the extracted features from different aspects and to eliminate redundant features so as to lower the dimensionality of feature space. Also, a large number of comparative experiments are made and it is proved that the several proposed methods are superior to several feature selection methods in the existing literatures greatly.Rough set theory can only deal with discreted attributes. The existing discretization definition based on cut-splitting is extended to generalized discretization definition based on class-separability. A novel method is presented to discretize interval-valued continuous attributes that cannot be processed effectively by the existing discretization methods. Also, time complexity of themethod is analyzed qualitatively.In the process of feature selection using class-separability, satisfactory rate of feature set and resemblance coefficient, a novel quantum genetic algorithm is proposed to search the best feature subset from original feature set. Comparing with traditional genetic algorithms, the novel quantum genetic algorithm has stronger searching capability, faster convergence speed, shorter computing time and better capability to avoid too-early convergence.4 Four classifier design methods, including Rough Set Neural Network (RSNN), Combination Support Vector Machine (CSVM), Huffman Tree Support Vector Machine (HTSVM) and Rough Set Support Vector Machine (RS-SVM), are proposed to recognize radar emitter signals automatically. The validities and superiorities of the 4 classifiers are shown in the comparative experiments.A large number of comparative experiments are made and several conclusions can be drawn: RSNN is superior to Radial Basis Function Neural Network (RBFNN), Probability Neural Network (PNN) and Back-Propagation Neural Network (BPNN) in classification capability and recognition efficiency; three popular multiclass classification Support Vector Machines (SVMs) including One-Against-All (OAA), One-Against-One (OAO) and Binary Tree Architecture (BTA), have better classification capabilities than RBFNN, PNN and BPNN; CSVM is superior to OAA, OAO and BTA greatly in classification capability and efficiency; HTSVM has faster recognition speed than OAA, OAO and BTA instead of lowering its classification performance; the introduction of rough set theory not only strengthens greatly classification capability of SVMs, but also enhances the generalization capability of SVMs.Unknown radar emitter signal recognition is also an important issue. This dissertation also discusses the applications of Competitive Learning Neural Network (CLNN) and Self-Organizing feature Map Neural Network (SOMNN) to recognizing radar emitter signals.5 One hundred and fifty-five radar emitter signals with different signal parameters are chosen to make recapitulative experiments to test the validity of the proposed model structure. Each of the 155 signals has one of 8 intrapulsemodulations. Experimental results show that the proposed model structure can recognize the advanced radar emitter signals more effectively than the current model structure, which proves that the introduced model structure is more practical than the current model structure. Also, CSVM and the feature subsets obtained by using respectively 4 feature selection methods including class-separability, satisfactory rate of feature set, resemblance coefficient and rough set theory are employed to make comparative experiments to identify the advanced radar emitter signals. About 88%, 85%, 93% and 91% correct recognition rates are achieved respectively, which testifies again that the validities and practicalities of the feature selection methods and CSVM. Moreover, some experiments are also made using CLNN and SOMNN respectively. Unsatisfying experimental results indicate that the performances of CLNN and SOMNN need improve further in future research.This work was supported partially by Electronic Warfare Technology Pre-research Foundation (NEWL51435QT220401), by the National EW Laboratory Foundation (No.51435030101ZS0502), and also supported by Doctorial Innovation Foundation (2003.12).更多还原