基于改进短时傅里叶变换的磁共振随机噪声消减方法

磁共振测深法(magnetic resonance sounding, MRS)具有无需钻探即可直接探测地下水含量的优势,但是极低的信噪比(signal-to-noise ratio, SNR)限制了该方法的大范围应用,目前的研究工作主要集中在消除MRS信号中的尖峰噪声和工频谐波噪声上,而随机噪声由于其无规律性导致难以消除,但是它的影响不容忽视.目前MRS随机噪声的消减常采用叠加法,需要重复采集数据叠加平均来达到消噪目的,探测时间长且消噪效果有限.针对这一问题,本文提出了一种改进的短时傅里叶变换方法,该方法通过处理单次采集的MRS包络信号来降低数据量,提高数据处理效率.改进的短时傅里叶变换方法采用解析信号代替常规短时傅里叶变换中的实值信号,提高MRS信号时频域瞬时幅度的准确度,得到MRS信号的高精度时频分布,然后提取时频域峰值幅度和峰值相位重构信号来消除随机噪声.仿真实验和实测数据处理结果表明,该方法能够直接处理单次采集数据,在信噪比高于–17.21 dB的情况下可有效提取MRS信号,实现随机噪声的压制,且与传统叠加法相比,信噪比最多可提高27.88 dB,均方根误差最多缩小36.44倍,参数估计值更加准确.本文的研究结果为利用MRS获取准确的地下水分布情况奠定了良好的基础.     

小波包节点分段阈值降噪在水声监听中的应用*

水声目标信号在发送、传播过程中,易受到环境噪声、系统自噪声等影响,因此水声监听过程中目标信号会掺杂大量噪声信息。为提高获取目标信号的准确性和可靠性,降低噪声,在已有小波分析基础上,提出小波包节点相对能量判断最优分解层,最优分解层节点系数分段阈值处理重构方法,实现水声监听信号分频段去噪。将0.1 kHz~8.4 k Hz实验数据按节点频率排序划分为5个强弱不同的频段信号实现消噪提取,结果表明该方法可将噪声信号与目标信号有效分离,与全局单一阈值相比,具有较好降噪能力。该方法打破了小波阈值去噪高频处理的局限性,提高了识别精度,改善了全局单一阈值去噪存在的短板,在鱼类分析识别、舰船监听、深海探测等方面具有一定的推广和应用价值。     

改进的小波阈值消噪法在湍流信号处理中的应用

湍流数据采集中有用信号往往不同程度地受到干扰噪声的污染,小波阈值消噪法一定程度上能将有用信号的高频成分和噪声区分开,将其应用于湍流信号消噪是相对合理的.针对现有小波阈值消噪法的不足,提出了一种改进的阈值函数和基于小波能量熵的阈值自适应选取方法.仿真实验表明,新方法具备连续性好、偏差小、自适应阈值选取等优点,不但可以实现高信噪比信号的合理消噪,而且能有效检测出强噪声干扰下的微弱有用信号.以两组实测湍流信号的消噪处理为例,考察了新方法在湍流信号处理中的消噪效果.结果表明,该方法能为湍流信号的后处理提供可靠的数据.     

基于时频分析的多目标盲波束形成算法

针对现有盲波束形成算法适用范围较窄, 多目标信号分离级联模式结构复杂、并联模式稳定性较差等问题, 提出一种基于时频分析的多目标盲波束形成算法. 该算法首先利用时频分析技术给出信号导向矢量的不确定集, 然后优化求解导向矢量的最优估计, 最后利用Capon方法实现多目标信号的并行输出. 理论分析及仿真结果表明, 该算法对信号特性没有特殊要求, 适用性较广, 性能稳定, 且输出信干噪比高于其他盲波束形成算法, 接近于最优Capon波束形成器.     

脉冲星信号的经验模态分解模态单元比例萎缩消噪算法

针对脉冲星信号的消噪问题, 提出了一种基于模态单元比例萎缩的经验模态分解(EMD)消噪方法. 利用经验模态分解将含噪脉冲星信号分解为一组内蕴模态函数(IMF), 将IMF中两个过零点间的部分定义为模态单元, 以模态单元为基本单位构造最优比例萎缩因子, 对IMF中的每个模态单元进行比例萎缩去噪, 进而建立基于模态单元比例萎缩的脉冲星信号滤波模型.对含噪脉冲星信号进行了消噪实验分析, 实验结果表明, 与小波硬阈值消噪法、比例萎缩小波消噪法和基于模态单元阈值的EMD消噪法相比, 该方法可以更有效地去除脉冲星信号中的噪声, 同时更好地保留了原信号中的有用细节信息. 关键词： 经验模态分解 脉冲星信号 模态单元比例萎缩 消噪     

互补型自适应滤波器在心磁信号处理中的应用

将心磁信号从干扰噪声中加以提取并有效地消除噪声干扰是心磁信号处理中尤为重要的环节 .从改进算法的角度出发，提出互补型自适应滤波器结构以实现心磁信号的消噪处理.该滤波器针对心磁这类非平稳信号进行设计，有效地解决了常规自适应滤波器应用于心磁信号处理时收敛速度和稳态误差的矛盾.通过仿真实验和心磁实验结果表明，该算法能有效地消除心磁信号的背景噪声和工频干扰噪声.同时该算法也可用于其他非平稳信号的消噪处理. 关键词： 自适应滤波 心磁图 最小均方误差     

激光云高测量去噪算法研究

针对激光雷达回波信号所含噪声的特点, 对比分析了传统去噪算法的优缺点, 重点就小波自适应阈值去噪方法和独立成分分析去噪方法进行了系统研究。为了验证这两种方法在激光云高测量信号去噪上的有效性和优劣性, 对包含高斯白噪声的模拟仿真信号进行了消噪, 并对半导体激光云高仪实际探测得到的大气回波信号进行了消噪处理, 最后对去噪结果进行了对比分析。仿真结果和实验结果表明, 这两种消噪方法均能够有效降低激光雷达回波信号中所含噪声, 并且独立成分分析消噪效果明显优于小波自适应阈值方法。     

小波消噪与二维相关红外光谱的质量优化

采用小波变换消噪技术首次应用到中药二维相关红外光谱的数据处理中。结果表明 ,经过消噪处理后 ,可以有效地去除噪声对二维相关红外光谱同步图的影响 ,优化谱图的质量。该技术的应用既增强了同步图中的信号峰 ,又分离了消噪前的重叠峰 ;不但提高了谱图的分辨率 ,而且还获得了更多的光谱信息。     

自由飞行昆虫运动变形测量中弱光条提取

通过分析昆虫自由飞行状态下结构光光条图像的特点,提出了一种用于提取昆虫运动变形测量中弱光条中心点的图像处理方法。该方法根据图像的灰度分布情况对图像进行分块,采用小波理论对图像消噪,利用模糊增强和小波同态滤波相结合的算法增强各图像的弱光条信号,再应用steger算法提取弱光条中心点的亚像素位置。给出几种算法实验结果的比较和分析,表明该方法成功实现了弱光条信号中心点的提取,并有效抑制了噪声和干扰。     

基于提升小波变换的阈值改进去噪算法在紫外可见光谱中的研究

在紫外可见光谱定量分析中,由于分光光度计内部的光学系统、光源、检测器、电子元器件,电路设计以及外部环境干扰等因素产生的随机噪声,严重影响光谱定量分析结果的准确性,为提高紫外可见光谱分析精度,需要对光谱数据进行去噪预处理。由于小波分析具有多分辨率,低熵性、去相关性等特点,基于小波分析的去噪算法优于传统的去噪算法,目前基于小波去噪的方法主要有模极大值去噪算法,系数相关去噪算法,阈值去噪算法,工程实际应用以Donoho的阈值去噪法最为常用。根据Donoho阈值消噪原理,提出一种基于提升小波变换的阈值改进算法,一方面使用提升小波变换,提升小波变换是第二代小波变换,继承了小波的多分辨率特性,并且不需要进行傅里叶变换,从而具有算法简单,速度快,实现简单的优点;另一方面提出了一种新的阈值函数,克服了硬阈值函数在阈值处不连续以及软阈值函数存在恒定偏差的问题,同时对阈值估计进行了调整,有利于信号小波系数的保留和噪声小波系数的剔除。对三组多金属离子混合溶液的实测紫外可见光谱信号,添加随机噪声后使用该方法进行去噪处理,并使用信噪比(SNR)和均方根误差(RMSE)进行去噪性能评价。试验结果表明,提出的算法优于Donoho的软硬阈值去噪算法,能够有效提高光谱信噪比和降低均方根误差,从而更好地消除光谱信号中的噪声和保留光谱信号中一些重要的细节特征,比较适合用于紫外可见光谱数据建模之前的去噪预处理,在紫外可见光谱信号分析中具有较好的应用前景。     

Blind Extraction of Chaotic Signals by Using the Fast Independent Component Analysis Algorithm

We report the results of using the fast independent component analysis （FastICA） algorithm to realize Mind extraction of chaotic signals. Two cases are taken into consideration： namely, the mixture is noiseless or contaminated by noise. Pre-whitening is employed to reduce the effect of noise before using the FastICA algorithm. The correlation coefficient criterion is adopted to evaluate the performance, and the success rate is defined as a new criterion to indicate the performance with respect to noise or different mixing matrices. Simulation results show that the FastICA algorithm can extract the chaotic signals effectively. The impact of noise, the length of a signal frame, the number of sources and the number of observed mixtures on the performance is investigated in detail It is also shown that regarding a noise as an independent source is not always correct.     

基于独立分量分析的红外线列扫描图像处理方法

独立分量分析(Independent component analysis,ICA)作为一种有效的盲源分离方法,其目的是从由传感器收集到的混合信号中分离出相互独立的源信号,使得这些分离出来的信号之间尽可能的相互独立。针对红外线列扫描图像,提出了一种基于ICA的图像增强方法,该方法能够有效地去除红外线列扫描图像的非均匀性干扰。阐述了ICA的基本原理,介绍了基于负熵判据的FastICA算法,给出了该方法的具体实现步骤及相应的实验处理结果。结果表明,利用该方法能够达到图像增强的目的。     

FCM-FastICA的点蚀声发射信号分离识别方法*

金属点蚀是一种破坏性和隐患较大的设备损伤形式。点蚀会产生声发射信号。点蚀过程中产生的多种声源类型会造成信号混叠,影响腐蚀进程的判断。针对点蚀信号混叠问题,提出一种模糊C均值聚类与快速独立分量分析算法相结合的点蚀信号分离识别方法。通过分析单、双点蚀声发射数据将点蚀分为钝化膜破裂阶段、点蚀诱导成核及发展阶段,由聚类确定信号类别并用快速独立分量分析分离混合信号,利用相关性函数验证分离效果。结果表明：单点蚀过程存在3类原信号,双点蚀过程存在7类信号,其中包含单个信号与混合信号;单个信号与原信号相关性极高,达到0.8以上,混合信号的分离分量与原信号相关性达到0.6以上,分离效果较好。该方法可对点蚀混合信号进行有效分离和识别,为腐蚀进程判断提供支持。     

基于最大信噪比测量两相流速度

为了克服噪声对信号的影响,提出一种利用最大信噪比和相关法测量两相流速度的方法.基于最大信噪比的信号分离方法是一种盲源信号分离方法,该算法利用统计独立信号完全分离时信噪比最大作为分离准则,它具有非常低的计算复杂度.这里首先利用盲源信号分离方法分别提取出上游和下游两相流信号,并据此求出两相流信号的相关函数曲线,由此求出信号的渡越时间,最后给出仿真实验的处理结果.实验结果表明该方法能够满足两相流速度的测量要求.     

卷积噪声环境下语音信号鲁棒特征提取

提出了一种基于独立分量分析(ICA)的语音信号鲁棒特征提取算法,用以解决在卷积噪声环境下语音信号的训练与识别特征不匹配的问题。该算法通过短时傅里叶变换将带噪语音信号从时域转换到频域后,采用复值ICA方法从带噪语音的短时谱中分离出语音信号的短时谱,然后根据所得到的语音信号短时谱计算美尔倒谱系数(MFCC)及其一阶差分作为特征参数。在仿真与真实环境下汉语数字语音识别实验中,所提算法相比较传统的MFCC其识别正确率分别提升了34.8%和32.6%。实验结果表明基于ICA方法的语音特征在卷积噪声环境下具有良好的鲁棒性。      

Blind extraction and localization of sound sources using point sources based approaches

This paper presents theoretical models for blind sound source localization and separation of the signals emitted by arbitrary point sources in free space. Source localizations are achieved by a model based approach that accounts for the spherical spreading of an acoustic wave and utilizes an iterative triangulation, based on the signals measured by a three-dimensional microphone array. Once source locations are determined, the source signals are separated by using the point source separation (PSS) method, which is valid for all types of signals, including harmonic, continuous, transient, random, narrowband and broadband. General solutions for signals separation are presented. Theoretically, PSS can reconstruct the individual source signals exactly. This is because it employs the free-space Green's function, which defines the exact correlation among individual sources and measurement microphones. To validate PSS, numerical simulations are carried out and results are compared with those obtained by FastICA (Independent Component Analysis) code. The impacts of various parameters such as the microphone configuration, type of source signals, signal to noise ratio, number of microphones and source localization errors on the quality of signals separation by using PSS and FastICA are examined. The advantages and disadvantages of PSS and FastICA are compared and discussed.     

Gradient method for blind chaotic signal separation based on proliferation exponent

A new method to perform blind separation of chaotic signals is articulated in this paper, which takes advantage of the underlying features in the phase space for identifying various chaotic sources. Without incorporating any prior information about the source equations, the proposed algorithm can not only separate the mixed signals in just a few iterations, but also outperforms the fast independent component analysis(FastICA) method when noise contamination is considerable.     

Algorithm for Optimal Estimation of Appearance Times of Pulsed Signals in Discrete Time

Using the methods of optimal nonlinear Markov filtering, we obtain an algorithm for optimal mean-square estimation of appearance times of random pulsed variations in signal parameters against the background of white Gaussian noise in discrete time. Linear difference equations are used to describe signals, noise, and the observed processes. Equations of the algorithm permitting real-time calculations of the a posteriori variances and optimal estimations of pulse-appearance times are obtained in the approximation of Gaussian conditional probability densities. We present simulation results for algorithm operation in the particular problem of estimating the appearance times of two pulsed signals having the known shapes and observed against noise background.     

频域盲源分离与波束形成结合抑制方向性强干扰方法

针对方向性强干扰严重影响无源声呐弱目标检测的问题,提出了频域盲源分离与波束形成结合的干扰抑制方法:以子带分解的方法实现宽带干扰抑制。对每个子带进行频域盲源分离,并估计出各分离信号的方位,将与给定强干扰方位匹配的分离信号置零,利用估计的解混矩阵和处理后的分离信号重构回阵元域信号并进行波束形成实现目标方位估计。声呐模拟器数据与海试数据验证结果表明,相对于传统零陷常规波束形成与零陷最小方差无失真响应波束形成方法有2 dB以上的增益,约6 dB的背景级降低,证明该方法在抑制方向性强干扰方面是有效的。      

An independent component analysis-based approach on ballistocardiogram artifact removing

Interest about simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data acquisition has rapidly increased during the last years because of the possibility that the combined method offers to join temporal and spatial resolution, providing in this way a powerful tool to investigate spontaneous and evoked brain activities. However, several intrinsic features of MRI scanning become sources of artifacts on EEG data. Noise sources of a highly predictable nature such as those related to the pulse MRI sequence and those determined by magnetic gradient switching during scanning do not represent a major problem and can be easily removed. On the contrary, the ballistocardiogram (BCG) artifact, a large signal visible on all EEG traces and related to cardiac activity inside the magnetic field, is determined by sources that are not fully stereotyped and causing important limitations in the use of artifact-removing strategies. Recently, it has been proposed to use independent component analysis (ICA) to remove BCG artifact from EEG signals. ICA is a statistical algorithm that allows blind separation of statistically independent sources when the only available information is represented by their linear combination. An important drawback with most ICA algorithms is that they exhibit a stochastic behavior: each run yields slightly different results such that the reliability of the estimated sources is difficult to assess. In this preliminary report, we present a method based on running the FastICA algorithm many times with slightly different initial conditions. Clustering structure in the signal space of the obtained components provides us with a new way to assess the reliability of the estimated sources.