平稳随机噪声中暂态信号的检测方法

提出一种在平稳随机噪声中检测暂态信号的循环双谱切片方法,同时给出仿真结果。     

基于栈式稀疏自编码器的新型干扰识别

为了有效应对频谱弥散干扰(SMSP)和切片组合干扰(C&I)两种新型干扰,提出了一种基于栈式稀疏自编码器的识别算法。该算法首先对干扰与否的雷达接收信号进行双谱分析;然后对双谱特征进行降维,得到高维样本。预训练阶段,构造稀疏自编码器神经网络模型进行无标签样本的预训练;然后根据有标签数据对该模型参数进行有监督微调;最后利用Softmax分类器完成新型干扰的识别。仿真实验证明该方法有较高的识别率,特别是相较于其他文献方法,该方法受信噪比影响最小且识别效果最佳。说明了深度学习方法应用于雷达新型干扰信号识别领域的可行性和优越性。     

双谱估计在导引头信号检测与处理中的应用

双谱估计是一种应用广泛的高阶谱估计。将双谱估计应用于超宽带导引头信号检测与处理系统中,为导引头信号处理中的平滑和滤波增添了一种新的方法。仿真结果表明该方法是有效的。     

Asymptotic Bias and Variance of Conventional Bispectrum Estimates for 2-D Signals

In this paper, we derive the asymptotic bias and variance of conventional bispectrum estimates of 2-D signals. Two methods have been selected for the estimation: the first one – the indirect method – is the Fourier Transform of the weighted third order moment, while the second one – the direct method – is the expectation of the Fourier component product. Most of the developments are known for 1-D signals and the first contribution of this paper is the rigorous extension of the results to 2-D signals. The calculation of the bias of the direct method is a totally original contribution. Nevertheless, we did all calculations (bias and variance) for both method in order to be able to compare the results. The second contribution of this paper consists of the comparison of the theoretical bispectrum estimate bias and variance with the measured bias and variance for two 2-D signals. The first studied signal is the output of a non-minimal phase linear system driven by a non-symmetric noise. The second signal is the output of a non-linear system with Gaussian input data. In order to assess the results, we performed the comparison for both methods with different sets of parameters. We show that the maximum bias coefficient is the one of the 1-D case multiplied by the dimensionality of the signal for both methods. We also show that the estimate variance coefficient is the 1-D case coefficient with a power equal to the signal dimensionality.Received October 21, 2002; Revised December 2003; Accepted March 25, 2004; First Online Version published in December 2004     

基于频谱编辑和调制信号双谱的齿轮裂纹故障诊断

齿轮产生裂纹故障时,其振动信号中的周期性故障冲击成分易被其他旋转部件的谐波信号以及背景噪声淹没,导致故障特征难以提取。针对这一问题,首先用改进的频谱编辑方法对原始信号中谐波分量进行抑制,提高信噪比;然后对编辑后的信号进行双谱分析,采用相邻切片融合平均的方法对双谱进行降噪,从降噪后双谱中选取故障特征频率明显的切片进行组合平均得到复合切片谱,进而提取出齿轮的故障特征。仿真和实验信号表明:在低信噪比条件下,频谱编辑与调制信号双谱相结合的方法能够有效抑制谐波信号以及白噪声的干扰,提取出故障特征,实现齿轮裂纹故障诊断。     

利用双谱提取固井模式特征信息

介绍了双谱的概念和有关性质,给出了基于双谱提取固井胶结特征信息的方法,并利用这种方法对实际信号进行了处理。研究了研究结果表明,基于双谱的特征信息提取方法能有效地从超声回波中获取固井胶结模式特征,使得不同胶结模式具有明显的可分性。     

高密度双谱分析技术在LSH深水地震资料中的应用研究

深水区的地震资料是采用长排列拖缆采集的,工区面积大,速度变化敏感,各向异性明显,传统人工速度分析技术不能满足工区的勘探开发需求,经研究,采用CGGVeritas的高密度双谱分析技术可以较好解决动校正问题,高效率高品质,提高了LSH深水地震资料的速度精度和密度,在全区得到推广应用,取得了很好的效果.     

短丝纤维卷绕牵伸齿轮箱故障信号的图像特征提取方法

针对短丝纤维卷绕牵伸齿轮箱故障信号不易提取的问题,提出了基于图像纹理信息的特征提取方法。通过对齿轮箱振动信号进行小波包双谱分析,获得具有稳定纹理信息的振动信号双谱图,采用基于小波变换对双谱图进行图像融合,提高图像的综合纹理特征。采用灰度共生矩阵的四个特征参数对振动信号的双谱图进行加权融合特征提取。在短丝生产线上对齿轮箱常见的齿轮破损和裂纹进行了实验分析,结果表明本文方法的故障识别率达到85%以上。     

Logarithm bispectrum-based approach to radar range profile for automatic target recognition

The paper starts by the definition of logarithm bispectrum and the presentation of its properties relative to target recognition. A relationship is established between the logarithm bispectrum feature of a target and that of its high resolution range profile. As a result, the criterion for target recognition, based on the relationship, is induced, and its scheme and pseudocode for implementation are given. Simulation with some acquired outfield data is given to show that the approach is effective and advantageous in both rapid recognition and easy computation.     

双谱核主元分析在气液两相流流型识别中的应用

针对压差波动信号的非线性和非高斯特性,提出了一种基于高阶谱和核主元分析相结合的流型识别方法。通过对气液两相流压差波动信号的双谱分析,提取了不同流型下信号的非高斯特征,以双谱分析核主元数字特征提取流型的特征,最后利用最小二乘支持向量机对流型进行智能识别。实验结果表明,提取的核主元特征反映了两相流的流动状态,最小二乘支持向量机可以有效地识别水平管道内的4种典型流型,整体识别率达到95%,为流型识别提供了一种有效的方法。