小波理论及其在防空武器系统中的应用
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摘要
目标识别和分类是防空武器系统雷达信息处理的一个关键环节,而从原始信号中提取雷达目标的有效特征又是目标识别、分类的重要步骤。而实际中雷达回波、声信号等都是非平稳、时变信号,目标特征一般隐含在信号的时域和频域中,采用常规信号处理方法很难提取目标的有效特征。小波分析是一种新的数学方法,它在保留Fourier分析优点的同时,又弥补了Fourier分析不能刻划L2以外的函数空间以及不能做局部分析等不足,它能对几乎所有的常见函数空间通过小波系数给出简单的刻画,也能用小波展开系数描述函数的局部光滑性质,特别是在信号分析中,由于它的局部分析及多分辨率特性的性能优越,因而在数据压缩与边缘检测方面比现有的手段更为有效。利用小波变换能够提取雷达目标的有效特征。本文对小波理论进行了较系统地分析研究,介绍了多种小波变换的定义和特性,介绍了小波包对信号的分解特性、小波包的空间分解方法,研究了遗传算法,研究了小波变换在防空武器系统中的应用。
第三章,介绍了小波函数逼近性的基本理论和小波包的概念及其性质；主要研究根据类别可分性准则,从小波包库中选择一个对分类最优的小波包基,从与小波包基对应的小波包系数中,选择一组具有最大可分性的系数作为模式的特征矢量,用于某防空武器系统雷达信号处理。介绍了遗传算法定义和特点,提出了一种改进的遗传算法。针对防空武器系统中雷达信号的特点,在三个分类准则(距离准则、散度准则和熵准则)和改进遗传算法的基础上,提出了最佳小波包基的选择方法,并且给出了实例验证。
第四章,介绍了防空武器系统雷达目标电磁散射特性及其防空武器系统雷达信号处理的一般方法,提出了一种雷达信号分辨的方法,此方法与基于FFT的信号分辨的方法相比,具有较强的频率分辨能力,对瞬变雷达信号有较强的分辨能力。针对传统防空C3I系统的数据融合算法运算量大,融合精度不高等不足,分析了雷达测量数据的小波变换系数在虚假点迹、漏失和漏报测量数据处的特点,提出了基于小波变换的防空武器系统雷达数据融合中的一种方法,为进一步研究多雷达跟踪问题,给出一种解决途径。利用小波包和遗传算法,提出了一种基于声信号特性的军用飞机识别算法,首先对样本数据进行快速小波变换,求最佳小波包基。将信号在最佳小波包基下进行分解,计算出信号的特征向量,对目标进行分类识别。给出了实验验证。
第五章提出了防空武器系统雷达目标一维距离像的数学模型,给出了雷达目标的一维纵向像的数学模型及特性和雷达目标一维高分辨距离像的获取方法,给出了基于一维距离像的防空武器系统雷达目标识别方法框架和基于多散射中心的雷达目标识别原理。通过对高分辨雷达回波信号的分析,在遗传算法的基础上,提出了一种高分辨雷达目标识别方法,由此进行目标识别。介绍了红外目标图像的特点,在第二代小波变换基本原理及其特点的基础上,提出了一种基于第二代小波变换的红外目标图像检测方法。对红外图像进行第二代小波分解,从而得到了图像的边缘细节,分别对每个高频分量进行滤波和增强处理。然后利用第二代小波逆变换重构原红外图像。通过模板的方式对图像进行滤波,得到二值化图像。在二值化图像中能够比较清晰地检测红外目标图像。利用该方法能够分割出单目标或多目标图像。研究了雷达目标受到箔条干扰时,雷达目标信号和箔条干扰信号的特点及其在小波变换不同尺度上的不同表现形态,充分利用雷达目标信号和箔条干扰信号在时域与频域的表现区别,提出了在箔条干扰背景下一种新的雷达目标识别方法。对雷达回波信号进行Mallat快速分解,计算相邻尺度上小波系数之间的相关系数,根据相关系数大小,对小波系数进行门限处理,然后由Mallat重构算法重构信号,得到目标信号,由此进行雷达目标检测。
本文提出的方法,均给出了仿真或实验结果,由仿真或实验结果看出本文提出的方法均是可行的,对我国防空武器系统中的信息处理和目标识别具有较大的理论意义和实际应用价值。
Target identification is an important link in the chain of information processing for air-defense. In target identification or classification, extracting effective identification features from original target signals is very important. But, for a great number of non-stationary or time varying signals, such as speech, echo, earthquake signals, ect. Identification features are often localized both in time and frequency, so thus extracting effective features from them by general transformation methods is very difficult. The wavelet analysis is a novel maths method. It keeps the advantages of the Fourier analysis, simultaneously making up the shortages that the Fourier analysis can not describe the function space beyond L2,the shortages that it can neither do the local analysis; the wavelet analysis can not only describe almost all the familiar functions by using the Wavelet coefficients, but also describe the local lubricant characters with expansion wavelet coefficient, especially in signal analysis, it is more effective than the existing methods in data compressing and margin detecting due to its predominant properties in local analysis. The effective features of target can be extracted by wavelet transforms.
This paper analyses and researches into wavelet theory in all sides, introducing both definitions and characteristics of Wavelet transforms with many kinds and the decomposed features which the wavelet package acts on signals and the space decomposed methods. Introducing basic conceptions of genetic algorithm. Introducing applications of wavelet transform in aerial defense weapon system.
Chapter 3 analyses and researches into wavelet approximation theory, introduces the basic definitions and characteristics of wavelet package and genetic algorithm, set forth an improved genetic algorithm. In the light of the features of radar signals in aerial defense weapon system, a chosen method of optimal wavelet package radix is brought forward on the basis of inheritance algorithm and improved genetic algorithm.
Chapter 4 deals with the basic conceptions of and generic and radar signal processing methods, a method for resolving radar signals is brought forward, which has strong frequency resolving power, thus it can be used in instantaneous radar signal distinguishing. In C3I system since traditional data fusion algorithms are more computationally complicated with fusion precision being lower, the character of wavelet transform of radar data in imprecise data is analyzed, a method for aerial defense weapon system data fusion is brought forward based on wavelet transforms.
In accordance with distinguishing features of target acoustic signals, using wavelet transform and genetic algorithm, a new method based on acoustic signals characteristics for identifying airplane is set forth, in this method target acoustic signal is taken as source signal to make characteristic extracted and pattern recognized. Computing wavelet transform of radar echo, using criterion and genetic algorithm, selecting optimal basis via given by training sample sets, the targets are recognized by effective features.
In Chapter 5, firstly, through analysis of high resolving radar echo signals, a mathematical model of radar-target-range-profile is brought forward, a radar target identifying method in aerial defense weapon system is set forth based on wavelet transforms and genetic algorithm, with the experimentation results indicating that such method has high identifying rate. Then based on wavelet transforms of the second generation, the characteristics of infrared target image are introduced, along with a detecting method for infrared target image put forth. First, infrared image is deposited by wavelet transforms. The edge information of the infrared target image is kept in wavelet transforms domain. Four wavelet decomposition components are processed. Second, infrared image is constructed by inverse wavelet transforms. Finally, the infrared image is filtered. The target is detected by two-norm image. The multi-targets can be segmented by using the method. The experiments show that this approach can achieve quite satisfactory results.
Thirdly, investigating the varying features of wavelet coefficients of radar target signals and chaff jamming signals, on basis of wavelet transforms and its correlation technique, a radar target identifying method under the foil strip jamming is brought forward by using the deference full deference between the radar target signal and foil strip jamming signal in time domain and frequency domain. First, the echo-signals are quickly decomposed by the Mallat-algorithm. Second, the correlation coefficient of near scales wavelet coefficient is calculated.The wavelet coefficient are rocessed through a threshold,and then the target signals are reconstructed and the radar target is recognized.
In this dissertation the methods have been simulated and tested with feasible results that have greater theoretical significance and actual applied value in regared to information processing and target identifying in our aerial defence weapon system.
第三章,介绍了小波函数逼近性的基本理论和小波包的概念及其性质；主要研究根据类别可分性准则,从小波包库中选择一个对分类最优的小波包基,从与小波包基对应的小波包系数中,选择一组具有最大可分性的系数作为模式的特征矢量,用于某防空武器系统雷达信号处理。介绍了遗传算法定义和特点,提出了一种改进的遗传算法。针对防空武器系统中雷达信号的特点,在三个分类准则(距离准则、散度准则和熵准则)和改进遗传算法的基础上,提出了最佳小波包基的选择方法,并且给出了实例验证。
第四章,介绍了防空武器系统雷达目标电磁散射特性及其防空武器系统雷达信号处理的一般方法,提出了一种雷达信号分辨的方法,此方法与基于FFT的信号分辨的方法相比,具有较强的频率分辨能力,对瞬变雷达信号有较强的分辨能力。针对传统防空C3I系统的数据融合算法运算量大,融合精度不高等不足,分析了雷达测量数据的小波变换系数在虚假点迹、漏失和漏报测量数据处的特点,提出了基于小波变换的防空武器系统雷达数据融合中的一种方法,为进一步研究多雷达跟踪问题,给出一种解决途径。利用小波包和遗传算法,提出了一种基于声信号特性的军用飞机识别算法,首先对样本数据进行快速小波变换,求最佳小波包基。将信号在最佳小波包基下进行分解,计算出信号的特征向量,对目标进行分类识别。给出了实验验证。
第五章提出了防空武器系统雷达目标一维距离像的数学模型,给出了雷达目标的一维纵向像的数学模型及特性和雷达目标一维高分辨距离像的获取方法,给出了基于一维距离像的防空武器系统雷达目标识别方法框架和基于多散射中心的雷达目标识别原理。通过对高分辨雷达回波信号的分析,在遗传算法的基础上,提出了一种高分辨雷达目标识别方法,由此进行目标识别。介绍了红外目标图像的特点,在第二代小波变换基本原理及其特点的基础上,提出了一种基于第二代小波变换的红外目标图像检测方法。对红外图像进行第二代小波分解,从而得到了图像的边缘细节,分别对每个高频分量进行滤波和增强处理。然后利用第二代小波逆变换重构原红外图像。通过模板的方式对图像进行滤波,得到二值化图像。在二值化图像中能够比较清晰地检测红外目标图像。利用该方法能够分割出单目标或多目标图像。研究了雷达目标受到箔条干扰时,雷达目标信号和箔条干扰信号的特点及其在小波变换不同尺度上的不同表现形态,充分利用雷达目标信号和箔条干扰信号在时域与频域的表现区别,提出了在箔条干扰背景下一种新的雷达目标识别方法。对雷达回波信号进行Mallat快速分解,计算相邻尺度上小波系数之间的相关系数,根据相关系数大小,对小波系数进行门限处理,然后由Mallat重构算法重构信号,得到目标信号,由此进行雷达目标检测。
本文提出的方法,均给出了仿真或实验结果,由仿真或实验结果看出本文提出的方法均是可行的,对我国防空武器系统中的信息处理和目标识别具有较大的理论意义和实际应用价值。
Target identification is an important link in the chain of information processing for air-defense. In target identification or classification, extracting effective identification features from original target signals is very important. But, for a great number of non-stationary or time varying signals, such as speech, echo, earthquake signals, ect. Identification features are often localized both in time and frequency, so thus extracting effective features from them by general transformation methods is very difficult. The wavelet analysis is a novel maths method. It keeps the advantages of the Fourier analysis, simultaneously making up the shortages that the Fourier analysis can not describe the function space beyond L2,the shortages that it can neither do the local analysis; the wavelet analysis can not only describe almost all the familiar functions by using the Wavelet coefficients, but also describe the local lubricant characters with expansion wavelet coefficient, especially in signal analysis, it is more effective than the existing methods in data compressing and margin detecting due to its predominant properties in local analysis. The effective features of target can be extracted by wavelet transforms.
This paper analyses and researches into wavelet theory in all sides, introducing both definitions and characteristics of Wavelet transforms with many kinds and the decomposed features which the wavelet package acts on signals and the space decomposed methods. Introducing basic conceptions of genetic algorithm. Introducing applications of wavelet transform in aerial defense weapon system.
Chapter 3 analyses and researches into wavelet approximation theory, introduces the basic definitions and characteristics of wavelet package and genetic algorithm, set forth an improved genetic algorithm. In the light of the features of radar signals in aerial defense weapon system, a chosen method of optimal wavelet package radix is brought forward on the basis of inheritance algorithm and improved genetic algorithm.
Chapter 4 deals with the basic conceptions of and generic and radar signal processing methods, a method for resolving radar signals is brought forward, which has strong frequency resolving power, thus it can be used in instantaneous radar signal distinguishing. In C3I system since traditional data fusion algorithms are more computationally complicated with fusion precision being lower, the character of wavelet transform of radar data in imprecise data is analyzed, a method for aerial defense weapon system data fusion is brought forward based on wavelet transforms.
In accordance with distinguishing features of target acoustic signals, using wavelet transform and genetic algorithm, a new method based on acoustic signals characteristics for identifying airplane is set forth, in this method target acoustic signal is taken as source signal to make characteristic extracted and pattern recognized. Computing wavelet transform of radar echo, using criterion and genetic algorithm, selecting optimal basis via given by training sample sets, the targets are recognized by effective features.
In Chapter 5, firstly, through analysis of high resolving radar echo signals, a mathematical model of radar-target-range-profile is brought forward, a radar target identifying method in aerial defense weapon system is set forth based on wavelet transforms and genetic algorithm, with the experimentation results indicating that such method has high identifying rate. Then based on wavelet transforms of the second generation, the characteristics of infrared target image are introduced, along with a detecting method for infrared target image put forth. First, infrared image is deposited by wavelet transforms. The edge information of the infrared target image is kept in wavelet transforms domain. Four wavelet decomposition components are processed. Second, infrared image is constructed by inverse wavelet transforms. Finally, the infrared image is filtered. The target is detected by two-norm image. The multi-targets can be segmented by using the method. The experiments show that this approach can achieve quite satisfactory results.
Thirdly, investigating the varying features of wavelet coefficients of radar target signals and chaff jamming signals, on basis of wavelet transforms and its correlation technique, a radar target identifying method under the foil strip jamming is brought forward by using the deference full deference between the radar target signal and foil strip jamming signal in time domain and frequency domain. First, the echo-signals are quickly decomposed by the Mallat-algorithm. Second, the correlation coefficient of near scales wavelet coefficient is calculated.The wavelet coefficient are rocessed through a threshold,and then the target signals are reconstructed and the radar target is recognized.
In this dissertation the methods have been simulated and tested with feasible results that have greater theoretical significance and actual applied value in regared to information processing and target identifying in our aerial defence weapon system.
引文
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