瞬态信号检测

介绍了瞬态信号波至点检测主要的方法:利用self-adaptation cancellation自适应对消原理的自适应权向量法(AWVM)、基于时频分析的Gabor变换法和利用小波分析的多尺度小波分解法以及单尺度小波变换法,并列出了其中关键的公式,使得它们自成整体;最后还分析它们的优缺点,并改进了检测函数,提出可供研究的方向。     

A novel signal feature extraction technology based on empirical wavelet transform and reverse dispersion entropy

Feature extraction is an important part of signal processing, which is significant for signal detection, classification, and recognition. The nonlinear dynamic analysis method can extract the nonlinear characteristics of signals and is widely used in different fields. Reverse dispersion entropy (RDE) proposed by us recently, as a nonlinear dynamic analysis method, has the advantages of fast computing speed and strong anti-noise ability, which is more suitable for measuring the complexity of signal than traditional permutation entropy (PE) and dispersion entropy (DE). Empirical wavelet transform (EWT), based on the theory of wavelet analysis, can decompose a complex non-stationary signal into a number of empirical wavelet functions (EWFs) with compact support set spectrum, which has better decomposition performance than empirical mode decomposition (EMD) and its improved algorithms. Considering the advantages of RDE and EWT, on the one hand, we introduce EWT into the field of underwater acoustic signal processing and fault diagnosis to improve the signal decomposition accuracy; on the other hand, we use RDE as the features of EWFs to improve the signal separability and stability. Finally, we propose a novel signal feature extraction technology based on EWT and RDE in this paper. Experimental results show that the proposed feature extraction technology can effectively extract the complexity features of actual signals. Moreover, it also has higher distinguishing ability for different types of signals than five latest feature extraction technologies.     

A Squared-Chebyshev wavelet thresholding based 1D signal compression

In this paper a square wavelet thresholding method is proposed and evaluated as compared to the other classical wavelet thresholding methods (like soft and hard). The main advantage of this work is to design and implement a new wavelet thresholding method and evaluate it against other classical wavelet thresholding methods and hence search for the optimal wavelet mother function among the wide families with a suitable level of decomposition and followed by a novel thresholding method among the existing methods. This optimized method will be used to shrink the wavelet coefficients and yield an adequate compressed pressure signal prior to transmit it. While a comparison evaluation analysis is established, A new proposed procedure is used to compress a synthetic signal and obtain the optimal results through minimization the signal memory size and its transmission bandwidth. There are different performance indices to establish the comparison and evaluation process for signal compression; but the most well-known measuring scores are: NMSE, ESNR, and PDR. The obtained results showed the dominant of the square wavelet thresholding method against other methods using different measuring scores and hence the conclusion by the way for adopting this proposed novel wavelet thresholding method for 1D signal compression in future researches.     

太赫兹雷达多声音信号活动检测技术

针对在环境中同时存在多个声源的场景下,传统声音获取方式难以正确检测到期望声源处声音活动的问题,提出一种基于太赫兹雷达的多声音信号活动检测方法,该方法利用太赫兹雷达波长短提升对于微小振动的探测灵敏度,同时利用大宽带所带来的高距离分辨力区分不同声源,在获得声音信号后进一步通过谱减-小波联合去噪方法提升信号质量用于后续声音信...     

Three-dimensional coordinates test method with uncertain projectile proximity explosion position based on dynamic seven photoelectric detection screen

To objectively obtain the three-dimensional coordinates of the projectile fuze proximity explosion when projectile intersects the head of missile target, we propose a dynamic seven photoelectric detection screen test method, which is made up of six plane detection screens and a flash photoelectric dynamic detection screen. The three-dimensional coordinates calculation model of the projectile proximity explosion position based on seven plane detection screens with dynamic characteristics is established. According to the relation of the dynamic seven photoelectric detection screen planes and the time values, the analytical function of the projectile proximity explosion position parameters under non-linear motion is derived. The projectile signal filtering method based on discrete wavelet transform is explored in this work. Additionally, the projectile signal recognition algorithm using an improved particle swarm is proposed. Based on the characteristics of the time duration and the signal peak error for the projectile passing through the detection screen, the signals attribution of the same projectile passing through six detection screens are analyzed for obtaining precise time values of the same projectile passing through the detection screens. On the basis of the projectile fuze proximity explosion test, the linear motion model and the proposed non-linear motion model are used to calculate and compare the same group of projectiles proximity explosion position parameters. The comparison of test results verifies that the proposed test method and calculation model in this work accurately obtain the actual projectile proximity explosion position parameters.     

基于最优Morlet小波的全信息能量熵提取及其在滚动轴承状态监测中的应用

为利用振动信号中隐含的冲击特征成分来反映轴承性能退化趋势,综合利用小波变换技术和全信息技术,提出一种基于最优Morlet小波变换的全信息能量熵提取方法．以最小Shannon熵优化Morlet小波形状参数,通过多源振动数据的小波变换系数,利用信息熵综合反映冲击特征能量在不同频带分布差异．滚动轴承全寿命数据的应用结果表明,全信息能量熵的变化趋势能够监测轴承状态的劣化过程,而伴随的早期故障检测可以提高轴承使用的安全性．     

小波相关特征尺度熵在滚动轴承故障诊断中的应用

将小波相关滤波方法与Shannon信息熵相结合,提出了一种故障检测与诊断的方法——小波相关特征尺度熵故障法。首先利用小波相关滤波方法提取滚动轴承故障振动信号的微弱故障信息特征,以求得信噪比较高的尺度域小波系数;然后结合Shannon信息熵理论给出了沿尺度分布的小波相关特征尺度熵定义及其计算方法。小波相关特征尺度熵能够定量表征不同尺度的能量分布,各尺度能量分布的均匀性可以反映滚动轴承的运行状态的差别,选取最能反映故障特征的小波相关特征尺度熵作为特征参数,通过所选取的小波相关特征尺度熵大小判断滚动轴承的工作状态和故障类型。实验证明该方法能有效地判断滚动轴承故障特征,为滚动轴承故障诊断提供了新的思路。     

基于EMD的改进小波阈值降噪法在超声信号处理中的应用

针对超声检测回波信号中的大量噪声,分析了超声检测回波信号的特性,考虑经验模式分解（EMD）和小波哗噪的优点,在改进阈值函数基础上提出一种基于EMD的小波阈值降噪方法．该方法利用EMD对超声信号进行分解,对高频分量用改进小波阈值函数方法进行处理,再结合低频分量重构得到降噪信号．仿真实验结果表明,该方法降噪效果优于小波软、硬阂值降噪,进一步提高了重建信号的信噪比,降低了其均方根误差,是一种可行的超声信号降噪方法．     

小波分析在雷达信号处理中的应用展望

小波分析在雷达信号处理中的应用研究,已成为近些年来信号处理学科的热点之一。介绍了小波分析的发展历史、研究现状,阐明了小波分析的基本概念,比较了各种小波处理方法的特点,详细讨论了小波在雷达信号处理中的诸多应用,指出了有待解决的各种问题,对小波分析在雷达信号处理中的应用提出了展望。     

基于小波包频带能量检测的神经网络故障诊断技术

利用小波包频带能量检测技术对观测信号进行处理,从中获取反映故障特征的信息,以此作为输入对网络进行训练,可对故障进行比较可靠的分类。同时介绍了利用小波包频带能量检测技术提取观测信号特征向量的方法和步骤,以及基于松散小波神经网络的故障诊断方法。最后结合某型导弹舵系统故障诊断的实例,给出仿真试验,证明了该方法的有效性。     

Moving horizon based wavelet de-noising method of dual-observed geomagnetic signal for nonlinear high spin projectile roll positioning

Phase-frequency characteristics of approximate sinusoidal geomagnetic signals can be used for projectile roll positioning and other high-precision trajectory correction applications. The sinusoidal geomagnetic signal deforms in the exposed and magnetically contaminated environment. In order to preciously recognize the roll information and effectively separate the noise component from the original geomagnetic sequence, based on the error source analysis, we propose a moving horizon based wavelet de-noising method for the dual-observed geomagnetic signal filtering where the captured rough roll frequency value provides reasonable wavelet decomposition and reconstruction level selection basis for sampled sequence; a moving horizon window guarantees real-time performance and non-cumulative calculation amount. The complete geomagnetic data in full ballistic range and three intercepted paragraphs are used for performance assessment. The positioning performance of the moving horizon wavelet de-noising method is compared with the band-pass filter. The results show that both noise reduction techniques improve the positioning accuracy while the wavelet de-noising method is always better than the band-pass filter. These results suggest that the proposed moving horizon based wavelet de-noising method of the dual-observed geomagnetic signal is more applicable for various launch conditions with better positioning performance.     

小波分析和神经网络结合的变速箱状态识别

针对变速箱故障信号的非平稳和时变特性，提出了小波分析和神经网络结合的变速箱状态识别方法。为了验证该方法的有效性，试验模拟了某型车辆变速箱正常、7216轴承滚动体点蚀及3挡被动齿轮严重磨损3种状态，以箱体振动信号作为分析信号，首先对信号应用小波阈值法降噪减少干扰，接着将小波分解系数单子带重构得到不同频带的信号分量，提取各频带能量作为特征向量输人神经网络进行状态识别，结果表明该方法能有效识别变速箱的3种状态。     

时频分析方法及其在自导信号检测中的应用

首先引入时频分析的概念,介绍了短时傅里叶变换、Wigner—Ville分布和连续小波变换的数学表示和性质.其次基于经典检测理论,讨论并比较了它们在高斯白噪声背景下对确知信号的检测问题.最后对它们在自导信号检测应用中的有关问题进行了分析,并指出了需进一步研究的方向.     

基于小波分析对网络协议指纹识别的研究

小波分析是当前信号、信息获取与处理领域(尤其是故障诊断分析与检测技术)中一个十分活跃且迅速发展的新领域,是当今国际研究热点,它同时具有理论深刻和应用十分广泛的双重意义。基于小波分析对网络协议进行指纹识别研究,利用小波变换把原始信号分解成多频段的信号,对信号进行塔式分解,通过计算处理每一层取得所有的有关Wi信息,提取指纹特征信息,并提出了检测方法和指纹识别系统的体系结构,对解决当今日益突出的网络安全问题进行有益尝试。     

基于小波包分析的电磁导波信号缺陷识别

在钢管的电磁导波缺陷检测中,导波信号会不同程度地受到环境噪声污染。针对该问题,提出基于小波包系数区域相关分析的信号处理方法,通过对有缺陷与无缺陷情况的钢管电磁导波检测信号进行小波包分解重构、滤波和区域相关法对比分析,有效地解决了电磁导波检测中钢管缺陷的判别问题。     

基于小波分层连通树结构的信号重构

基于小波树模型的压缩感知可以通过较少的观测量得到鲁棒的信号重构,但采用最优树逼近时存在复杂度大的问题。在证明分层后的小波树仍然具备连通树性质的基础上,提出了基于小波分层连通树结构的压缩重构算法,在与原观测量一致的情况下,保证了重构精度并且提高了重构效率。实验结果表明,改进算法相对于原算法在处理大尺度数据时,效率有明显改善。     

基于类别可分性准则的金属磁记忆信号小波能量谱特征提取研究

为了解决金属磁记忆信号小波能量谱特征存在的相关性和冗余性问题,利用类别可分性准则,在提取金属磁记忆信号小波能量谱的基础上,将能量谱特征进行变换提取最优特征向量。将能量谱特征向量、最优特征向量和低频特征向量作为支持向量机的特征输入量分别对不同检测区域的金属磁记忆信号进行识别。实验结果表明：最优特征向量能够减小小波能量谱特征的相关性和冗余性,有效提高支持向量机识别的准确率。     

基于Canny算子的小波域维纳滤波去噪方法

本文给出了一种利用Canny算子的小波域局部维纳滤波的图像去噪方法。先用二维小波对图像进行分解，在第一重维纳滤波时使用椭圆形方向窗估计信号的方差。在第二重维纳滤波时，使用Canny算子把图像分成边缘区域和非边缘区域，然后结合圆形窗估计信号的方差，再利用局部维纳滤波进行去噪。实验结果表明了本文方法的有效性。     

雷达信号调制类型自动识别的一种新方法

雷达辐射源信号调制类型的自动识别是电子战系统的关键技术之一.深入分析了小波变换法提取信号脉内特征的基本原理,提出一种基于小波变换法提取的脉内特征自动判别信号调制类型的新方法.仿真结果表明,在满足一定的信噪比条件下,新方法能准确识别给定信号的调制类型.     

对流层散射信道传递时间信号的抖动抑制模型

对流层散射信道传递时间同步信号过程中,信道的多径效应及噪声会引起信号抖动。为抑制抖动、提高时间同步精度,引入经验模态分解和小波阈值组合的抖动抑制模型。组合抖动抑制模型中,首先根据经验模态分解原理对原始信号进行分解,然后利用小波阈值对分解得到的各分量进行处理,最终利用处理结果重构信号。为提高组合模型的抖动抑制效果和原始信号的保持能力,利用连续性好、柔和度高的阈值函数对小波阈值中的传统阈值函数进行改进。利用实测数据验证模型的结果表明,在抖动抑制和信号保留方面,组合模型较单一模型以及常用的Kalman模型优势明显。