噪声背景下双输入时延混合系统的盲源分离

噪声背景下应用盲分离技术恢复源信号是盲信号处理的难点之一,本文主要研究了双输入时延有噪混合模型的盲分离方法,和传统的盲分离算法相比,该方法可以有效地利用多阵元的观测信号,对加性噪声具有相当的抑制作用。     

瞬时混合盲源分离和最优波束形成的关系研究

最优波束形成和盲源分离是近几十年迅速发展起来的两种重要阵列信号处理方法,在瞬时混合情况下它们具有相似的信号模型,所以本文研究了二者之间的关系。指出,当不存在噪声时,多数盲源分离算法构造的是最优波束形成;当存在噪声时,可以利用盲源分离算法估计的信号导向向量设计最优波束形成。计算机仿真实验验证了这一结论。     

复杂电磁环境下的信号盲分离方法

介绍了基于盲源分离的信号处理基本思想,阐述了ICA,SOBI,AMUSE三种典型算法的原理、特点,通过仿真实验研究了这三种盲源分离算法对于常见波形信号和真实信号的分离性能及其抗噪能力,验证了算法的有效性和可行性。三种方法对于抑制干扰信号,进行信号的有效提取具有实际意义和使用价值。     

均匀度估计的噪声源盲分离算法研究

盲信号分离技术是源输入未知时识别噪声源的一种有力手段。源的独立性常是应用盲分离算法的一个基本要求。但由于概率密度函数一般未知且估计繁琐,传统盲分离算法对源信号的独立性较难检验。为此,从信号独立性分析出发,理论上推导了随机变量的独立性和其概率分布函数的联合分布之间的关系,提出了一种独立性度量指标均匀度的估计算法,并给出了相应的盲分离算法。利用电机和海水泵的振动信号分离试验对方法进行了验证,并与现有的半熵盲分离算法进行比较,结果表明在分离效果和计算时间方面均优于现有的方法。充分说明了本文算法的有效性。      

EEMD与EFICA在单通道复合故障诊断中的应用

针对转子不对中和滚动轴承微弱损伤的复合故障诊断问题,提出了一种基于平均经验算法(Ensemble Empirical Mode Decomposition,EEMD)和高效快速独立分量分析(Efficient Variant of FastICA,EFICA)的盲源分离故障诊断方法。利用EEMD算法将单通路复合故障信号分解成多个不同信号特征的本征模函数(Intrinsic Mode Function,IMF),解决了盲源分离中的欠定问题。在此基础上利用EFICA算法对各个不同信号特征的IMF进行故障特征分离。通过仿真实验和转子实验台的实验结果,表明该算法可以有效分离出各个不同的故障特征。     

彩色视频提取心率信号的联合近似对角化算法

为了实现从彩色视频中有效提取心率信号,实时检测人体心率,提出使用基于特征矩阵联合近似对角化的盲源分离算法,从RGB彩色可见光视频信号中分离周期信号,为周期信号处理和周期分析得出心率提供数据基础,从而提高非接触式心率测量的准确性。同时利用matlab编写程序进行仿真实验,实验结果表明该算法能有效分离振幅在干扰信号波动范围的0.2及以上的源信号,分离信号与已知源信号相关系数高达0.8。     

基于总体最小二乘算法的平稳声信号二阶盲分离方法

利用声源信号相互独立的已知条件,提出分离平稳声信号的总体最小二乘二阶盲分离算法,该算法在联合近似对角化相关函数矩阵的约束下估计分离矩阵,在同时考虑估计矩阵与观测信号误差的情况下给出修正模型,在修正模型总体误差最小时分离源信号。提出的算法具有无需选择非线性函数,不存在收敛到局部最小值与计算速度快的特点,分离的声源信号具有较小的失真。半消音室电机噪声分离实验验证了上述算法的有效性。     

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.     

基于非线性时频掩蔽的语音盲分离方法

针对语音信号的欠定卷积混合模型,利用独立语音在时频域上的近似W-分离正交性(W-DO),提出了一种基于非线性时频掩蔽的盲分离方法。首先对多传声器观测信号在时频域上进行规范化处理,使混合信号在每个时频槽的表示与频率无关,然后采用动态聚类算法获取时频槽对应的活跃源信息,选择关于簇中心偏角的非线性函数进行时频掩蔽,从而实现语音信号的盲分离。该方法解决了经典频域盲分离算法中的频率置换问题,能有效抑制分离矩阵的空间方向扩散。仿真实验表明,与BLUES方法相比具有更优的分离性能,信噪比增益平均增加1．58 dB。     

一种基于模式识别的多路盲语音提取方法

盲分离算法能在缺少混合系统参数的条件下仅由观测信号估计初始源,但分离信号存在固有的排列模糊性,这往往导致两次批处理过程中同一信号"对不准",因此很难获得连续的源信号。本文针对盲声源分离中存在的相同问题,根据语音和其他音频信号的特征差异,提出一种修正的自相关函数并以其值作为一个特征基元来表征声音信号的时序相关特性,同时用平均声门波形状参数作为另一个特征基元来表征语音产生的生理效应。以这两个参数作为识别不同音频信号的二维模式特征,采用一种模糊聚类算法提取多路盲分离语音。本方法有效克服了批处理盲声源分离中的信号排列顺序的不确定性,并通过选择合适的阈值提取多路连续语音。仿真给出了5路混合音频信号中盲提取两路连续语音的实验结果。     

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。      

A blind separation algorithm for underdetermined convolutional mixed communication signals based on time–frequency soft mask

This paper focuses on the separation for time–frequency (TF) overlapped communication signals received by the sensors. A novel blind separation strategy is proposed to improve the poor performance of signal separation by traditional algorithms for convolutional mixtures in underdetermined cases. Firstly, the number of sources and cluster centers are obtained in the sparse domain by combining the density peak clustering (DPC) with fuzzy c-means (FCM) clustering algorithm; Then the GMM clustering algorithm is applied to calculate the membership degree of the source signal in the mixed signals, so as to construct a TF soft mask matrix to more precisely carry out separation for TF overlapped signals. In this paper, the separation simulations are conducted with the digital modulation signals of 2ASK, BPSK, QPSK, etc. The results show that the algorithm proposed in this paper has better anti-aliasing and anti-noise performance than the comparison algorithms.     

Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation

Energy separation algorithm is good at tracking instantaneous changes in frequency and amplitude of modulated signals, but it is subject to the constraints of mono-component and narrow band. In most cases, time-varying modulated vibration signals of machinery consist of multiple components, and have so complicated instantaneous frequency trajectories on time-frequency plane that they overlap in frequency domain. For such signals, conventional filters fail to obtain mono-components of narrow band, and their rectangular decomposition of time-frequency plane may split instantaneous frequency trajectories thus resulting in information loss. Regarding the advantage of generalized demodulation method in decomposing multi-component signals into mono-components, an iterative generalized demodulation method is used as a preprocessing tool to separate signals into mono-components, so as to satisfy the requirements by energy separation algorithm. By this improvement, energy separation algorithm can be generalized to a broad range of signals, as long as the instantaneous frequency trajectories of signal components do not intersect on time-frequency plane. Due to the good adaptability of energy separation algorithm to instantaneous changes in signals and the mono-component decomposition nature of generalized demodulation, the derived time-frequency energy distribution has fine resolution and is free from cross term interferences. The good performance of the proposed time-frequency analysis is illustrated by analyses of a simulated signal and the on-site recorded nonstationary vibration signal of a hydroturbine rotor during a shut-down transient process, showing that it has potential to analyze time-varying modulated signals of multi-components.     

Detection of weak signals using fast projection algorithms

The problem of detecting weak signals in complex noise situations using projection adaptive algorithms is considered. The existing algorithms are analyzed, and a novel algorithm oriented at detecting weak signals is proposed. Results of the algorithm’s operation are demonstrated in a simulated noise situation consisting of interference signals with different intensities under the assumption of their multipath propagation and scattering. The proposed algorithm is compared with the well-known classical Capon algorithm, and a significant reduction in the contact loss time as applied to a low-noise target near strong interference sources is demonstrated.     

混沌噪声背景下微弱脉冲信号的检测及恢复

构建了一种在混沌噪声背景下检测并恢复微弱脉冲信号的模型.首先,基于混沌信号的短期可预测性及其对微小扰动的敏感性,对观测信号进行相空间重构、建立局域线性自回归模型进行单步预测,得到预测误差,并利用假设检验方法从预测误差中检测观测信号中是否含有微弱脉冲信号.然后,对微弱脉冲信号建立单点跳跃模型,并融合局域线性自回归模型,构成双局域线性(DLL)模型,以极小化DLL模型的均方预测误差为目标进行优化,采用向后拟合算法估计模型的参数,并最终恢复出混沌噪声背景下的微弱脉冲信号.仿真实验结果表明本文所建的模型能够有效地检测并恢复出混沌噪声背景中的微弱脉冲信号.     

基于改进非局部均值的CPMG信号去噪算法

采用低场核磁共振技术进行检测时,接收到的回波信号微弱且信噪比低,真实的信号容易淹没在背景噪声中,严重影响到后续的反演等操作的准确性．针对这一问题,提出利用非局部均值滤波算法对CPMG(Carr Purcell Meiboom Gill)回波信号进行降噪的方法．首先,对算法中至关重要的参数选择的方法进行分析,提出了利用Stein无偏风险估计的自适应参数选取方法;然后,根据回波信号的特性对算法进行改进,即利用信号点数据方差的不同,自适应地求取各点进行非局部均值滤波时的相似窗宽度;最后,求取利用最优参数进行降噪后的CPMG回波信号．对仿真数据和真实数据的反演结果对比分析表明,该改进的非局部均值滤波算法能够取得更好的滤波效果,能够获得较优的反演谱．     

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。     

Reflectivity function reconstruction using the frequency deconvolution technique

Deconvolution of ultrasonic echo signals improves resolution and quality of ultrasonic images. A frequency deconvolution algorithm depends on the Fast Fourier transform is proposed for ultrasonic data. The stability of the algorithm and the influence of the truncation effect on the deconvoluted results were investigated with respect to the duration time of reflectivity function reconstruction and the signal to noise ratio. Reliability of the separation of reconstructing the reflectivity of a biological tissue is estimated by frequency deconvolution of the echo ultrasound signals.