被动声目标识别理论研究
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摘要
本文以智能雷为应用背景,在深入分析和研究典型声目标信号产生机理和特性的基础上,对被动声目标探测与识别的关键技术进行了研究,给出了相应的理论和算法,研究成果可为被动声识别技术的理论发展和工程化提供参考。
基于当前技术基础,对典型战场声目标信号特性进行了研究,总结和分析了坦克、直升机、战斗机和装甲车四种典型声目标的信号产生机理和特性。
信号预处理是提高识别准确率的重要技术途径,主要研究小波变换和EMD两种信号处理方法对于单传声器和多传声器信号的降噪方法,提出一种基于EMD的自适应噪声抵消算法和两种基于时延矢量封闭准则的多传声器信息融合消噪算法：
1)提供了一种新的参考信号选取方法,鉴于EMD分解特性,采用其高频IMF分量作为自适应噪声抵消器的参考噪声输入,与小波全局阈值和分层阈值降噪信号进行对比,实验结果证明该算法具有更好的降噪效果；
2)根据多传声器时延估计特性,提出了时延矢量封闭准则,结合多传声器系统小波系数时延估计特性和信息融合理论,提出了多传声器系统的三角时延矢量误差,并用多传声器综合支持度定义时延阈值,对信号进行滤波；发展了EMD对多传声器信号降噪的算法,借鉴对IMF分量进行加权的消噪思想,依据时延矢量封闭准则计算时延矢量误差,用多传声器综合支持度定义IMF分量有效性的判据,得到IMF函数的权重矩阵,最后根据IMF函数及其权重矩阵得到重构后的信号。理论分析和实验结果表明两种降噪方法均表现出良好的多传声器滤波特性。
声信号的特征提取与选择方法是声识别的关键技术之一,主要研究了信号的五种特征提取方法,对所得特征向量进行类别可分性判别,给出各类特征向量的整体可分性、单类目标特征向量可分性及两两目标之间特征向量可分性。在研究过零点特征、AR模型参数、核Fisher判别分析的特征提取方法基础上,提出了一种特征选择方法和两种特征提取方法：
1)提出基于距离可分性测度的显著性特征选择方法,首先选择能够反映类别可分性的距离测度对特征向量进行处理,然后构造显著性函数,对可分性测度值进行选择,满足显著性条件的测度对应的特征向量为有效特征向量；
2)提出基于EMD和能量比的特征提取方法,非平稳、非线性信号经EMD分解得到平稳、线性的IMF分量,对各个IMF分量进行FFT,求得信号的幅值谱,获得各IMF分量相对于原信号的能量比,对其进行归一化,将归一化后的能量比作为新的特征向量,其类别可分性判据和后续分类识别结果均证明这种特征向量的有效
3)提出基于多尺度分频的特征提取方法,根据多尺度理论提出信号频域的多尺度分频思想,将信号在不同尺度频段上的频率进行归整,组成新的特征向量,实验结果表明该方法提供了一种简单有效并适合工程应用的特征提取方法。
在声目标识别分类器设计方面,提出一种基于相关性系数的变权模板匹配分类器,选择适于分类识别的相似性系数函数,依据相关性系数通过离线训练获得特征向量的模板及其权重,并设定合适的阈值对目标进行分类；同时将粒子群神经网络和支持向量机两类分类器应用于声目标的分类与识别,并给出最后的分类结果。
为了验证本文提出的声识别算法的有效性和工程可行性,研究了声目标识别硬件系统的结构,开发了试验样机,并设计了室内和室外的声速检测试验和信号采集试验,为声识别获得大量试验样本,最后选择适于工程应用的简易识别算法采用试验样机对目标类型进行判别,实验结果证明试验样机具有一定的工程应用优势,可为装备武器做准备。
Based on the application background of intelligent mine and analysis of classical acoustic generation mechanism & characteristics, the key technology of passive acoustic target detection and recognition is studied, corresponding theories and algorithms is proposed. The achievements in this paper exist as a theories development and engineering operation reference of passive acoustic recognition.
On the basis of current technology development, the characteristics of classical acoustic in battlefield are studied, and the signal generation mechanism and characteristics of four classical acoustic targets (tank, helicopter, fighter and armored vehicle) are summarized and analysed.
Signal preprocessing is one important technical way to improve the accuracy of recognition rate. The denoising algorithm in this paper is based on wavelet transform and EMD. An adaptive noise cancelling algorithm based on EMD and two denoising algorithms of multi-microphone information fusion based on time delay vector close rule (TDVCR) are proposed:
1) A new reference signal extraction algorithm is proposed. Whereas the characteri-stics of EMD is adaptive, the higher IMF components are considered as the reference input of adaptive noise canceling implement. The denoising results are compared with the signlas filtered by wavelet global thresholds and layered thresholds, the exprements represent the EMD adaptive noise cancelling algorithm can reach better denoising property;
2) According to the characteristics of multi-microphone time delay estimation, time delay vector close rule is proposed. Triangular time delay vector errors are put forward combining with information fusion and TDVCR of wavelet coefficients, and are weighted by time delay error thresholds defined by multi-microphone integrated support degree. The denoising signals are reconstructed by above steps. The multi-microphone denoising algorithm based on EMD is developed, using for reference of weighted IMF components. The time delay vector errors are calculated by TDVCR and also weighted by time delay thresholds defined by multi-microphne integrated support degree, and the weighting matrix of IMF function is got. The denoising signal is reconstructed with IMF function and weighting matrix. The theoretical analysis and experiments represent two denoising algorithms here show better filtering properties.
Feature extraction and feature selection is another key technology of acoustic recognition. Five algorithms of feature extraction are studied, category separability norm of all engivectors are given, then the global separability and single target separability and two-target separability are researched. Based on the zero-crossing theory and AR model parameter and kernel fisher criterion analysis, one feature selection algorithm and two feature extraction algorithms are proposed:
1) Remarkable feature selection based on distance separability measure is proposed. The engivectors are processed by distance separability measure. The remarkable function is constructed and used for selecting separability value. The engivectors corresponding to the selected separability value are considered as effective feature.
2) Feature extraction based on EMD and power ratio is proposed. The IMF components of signals are processed by FFT, the spectrum of IMFs are got. Normalized the power ratio of IMF spectrum and original signal spectrum, and making the power ratio as engivector of signals. The category separability norm and recognition rate show that the feature extraction algorithm based on EMD and power_ratio is effective.
3) Feature extraction based on multiscale frequency division is proposed. The multiscale frequency division is put forward based on multiscale theory. The signals are normalize at different frequency section, and created new engivectors. The experimental results represent the feature extraction algorithm based on multiscale frequency division is simple & effective and can be used for engineering application.
At the section of classifier design for acoustic recognition, a model matching classifer based on comparability coefficients is proposed. The models of engivectors and corresponding weights are decided by offline traiing corresponding to the proper comparability function selected, and are used for classifying the target by setting thresholds. The particle neural network and SVM are applied to the acoustic target classification and recognition. The experimental results are given in the paper.
In order to verify the effectivity and engineering feasibility of recognition algorithm, the recognition hardware system is designed and developed. The measurement experiments of sound velocity inside and outside are designed. The signal collecting experiments are carried by multi-microphone array inside and outside, which provide lots of signals for recogniton. The simplicity recognition algorithm is used in this hardware system. The experiements represent the hardware system has advantages of engineering application, which is prepare for arming the weapon.
基于当前技术基础,对典型战场声目标信号特性进行了研究,总结和分析了坦克、直升机、战斗机和装甲车四种典型声目标的信号产生机理和特性。
信号预处理是提高识别准确率的重要技术途径,主要研究小波变换和EMD两种信号处理方法对于单传声器和多传声器信号的降噪方法,提出一种基于EMD的自适应噪声抵消算法和两种基于时延矢量封闭准则的多传声器信息融合消噪算法：
1)提供了一种新的参考信号选取方法,鉴于EMD分解特性,采用其高频IMF分量作为自适应噪声抵消器的参考噪声输入,与小波全局阈值和分层阈值降噪信号进行对比,实验结果证明该算法具有更好的降噪效果；
2)根据多传声器时延估计特性,提出了时延矢量封闭准则,结合多传声器系统小波系数时延估计特性和信息融合理论,提出了多传声器系统的三角时延矢量误差,并用多传声器综合支持度定义时延阈值,对信号进行滤波；发展了EMD对多传声器信号降噪的算法,借鉴对IMF分量进行加权的消噪思想,依据时延矢量封闭准则计算时延矢量误差,用多传声器综合支持度定义IMF分量有效性的判据,得到IMF函数的权重矩阵,最后根据IMF函数及其权重矩阵得到重构后的信号。理论分析和实验结果表明两种降噪方法均表现出良好的多传声器滤波特性。
声信号的特征提取与选择方法是声识别的关键技术之一,主要研究了信号的五种特征提取方法,对所得特征向量进行类别可分性判别,给出各类特征向量的整体可分性、单类目标特征向量可分性及两两目标之间特征向量可分性。在研究过零点特征、AR模型参数、核Fisher判别分析的特征提取方法基础上,提出了一种特征选择方法和两种特征提取方法：
1)提出基于距离可分性测度的显著性特征选择方法,首先选择能够反映类别可分性的距离测度对特征向量进行处理,然后构造显著性函数,对可分性测度值进行选择,满足显著性条件的测度对应的特征向量为有效特征向量；
2)提出基于EMD和能量比的特征提取方法,非平稳、非线性信号经EMD分解得到平稳、线性的IMF分量,对各个IMF分量进行FFT,求得信号的幅值谱,获得各IMF分量相对于原信号的能量比,对其进行归一化,将归一化后的能量比作为新的特征向量,其类别可分性判据和后续分类识别结果均证明这种特征向量的有效
3)提出基于多尺度分频的特征提取方法,根据多尺度理论提出信号频域的多尺度分频思想,将信号在不同尺度频段上的频率进行归整,组成新的特征向量,实验结果表明该方法提供了一种简单有效并适合工程应用的特征提取方法。
在声目标识别分类器设计方面,提出一种基于相关性系数的变权模板匹配分类器,选择适于分类识别的相似性系数函数,依据相关性系数通过离线训练获得特征向量的模板及其权重,并设定合适的阈值对目标进行分类；同时将粒子群神经网络和支持向量机两类分类器应用于声目标的分类与识别,并给出最后的分类结果。
为了验证本文提出的声识别算法的有效性和工程可行性,研究了声目标识别硬件系统的结构,开发了试验样机,并设计了室内和室外的声速检测试验和信号采集试验,为声识别获得大量试验样本,最后选择适于工程应用的简易识别算法采用试验样机对目标类型进行判别,实验结果证明试验样机具有一定的工程应用优势,可为装备武器做准备。
Based on the application background of intelligent mine and analysis of classical acoustic generation mechanism & characteristics, the key technology of passive acoustic target detection and recognition is studied, corresponding theories and algorithms is proposed. The achievements in this paper exist as a theories development and engineering operation reference of passive acoustic recognition.
On the basis of current technology development, the characteristics of classical acoustic in battlefield are studied, and the signal generation mechanism and characteristics of four classical acoustic targets (tank, helicopter, fighter and armored vehicle) are summarized and analysed.
Signal preprocessing is one important technical way to improve the accuracy of recognition rate. The denoising algorithm in this paper is based on wavelet transform and EMD. An adaptive noise cancelling algorithm based on EMD and two denoising algorithms of multi-microphone information fusion based on time delay vector close rule (TDVCR) are proposed:
1) A new reference signal extraction algorithm is proposed. Whereas the characteri-stics of EMD is adaptive, the higher IMF components are considered as the reference input of adaptive noise canceling implement. The denoising results are compared with the signlas filtered by wavelet global thresholds and layered thresholds, the exprements represent the EMD adaptive noise cancelling algorithm can reach better denoising property;
2) According to the characteristics of multi-microphone time delay estimation, time delay vector close rule is proposed. Triangular time delay vector errors are put forward combining with information fusion and TDVCR of wavelet coefficients, and are weighted by time delay error thresholds defined by multi-microphone integrated support degree. The denoising signals are reconstructed by above steps. The multi-microphone denoising algorithm based on EMD is developed, using for reference of weighted IMF components. The time delay vector errors are calculated by TDVCR and also weighted by time delay thresholds defined by multi-microphne integrated support degree, and the weighting matrix of IMF function is got. The denoising signal is reconstructed with IMF function and weighting matrix. The theoretical analysis and experiments represent two denoising algorithms here show better filtering properties.
Feature extraction and feature selection is another key technology of acoustic recognition. Five algorithms of feature extraction are studied, category separability norm of all engivectors are given, then the global separability and single target separability and two-target separability are researched. Based on the zero-crossing theory and AR model parameter and kernel fisher criterion analysis, one feature selection algorithm and two feature extraction algorithms are proposed:
1) Remarkable feature selection based on distance separability measure is proposed. The engivectors are processed by distance separability measure. The remarkable function is constructed and used for selecting separability value. The engivectors corresponding to the selected separability value are considered as effective feature.
2) Feature extraction based on EMD and power ratio is proposed. The IMF components of signals are processed by FFT, the spectrum of IMFs are got. Normalized the power ratio of IMF spectrum and original signal spectrum, and making the power ratio as engivector of signals. The category separability norm and recognition rate show that the feature extraction algorithm based on EMD and power_ratio is effective.
3) Feature extraction based on multiscale frequency division is proposed. The multiscale frequency division is put forward based on multiscale theory. The signals are normalize at different frequency section, and created new engivectors. The experimental results represent the feature extraction algorithm based on multiscale frequency division is simple & effective and can be used for engineering application.
At the section of classifier design for acoustic recognition, a model matching classifer based on comparability coefficients is proposed. The models of engivectors and corresponding weights are decided by offline traiing corresponding to the proper comparability function selected, and are used for classifying the target by setting thresholds. The particle neural network and SVM are applied to the acoustic target classification and recognition. The experimental results are given in the paper.
In order to verify the effectivity and engineering feasibility of recognition algorithm, the recognition hardware system is designed and developed. The measurement experiments of sound velocity inside and outside are designed. The signal collecting experiments are carried by multi-microphone array inside and outside, which provide lots of signals for recogniton. The simplicity recognition algorithm is used in this hardware system. The experiements represent the hardware system has advantages of engineering application, which is prepare for arming the weapon.
引文
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