共查询到19条相似文献,搜索用时 500 毫秒
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盲源分离(BSS)问题是在缺少先验知识的情况下,从接收到的观测信号中恢复统计独立的源信号。独立分量分析(ICA)方法把多维随机矢量转换为尽可能统计独立的分量,是现代解决盲源分离问题最主要的方法之一。本文给出了一种基于峰度的盲源分离算法,与用Comon的方法求解Givens矩阵相比,结构清晰、实现简单,而且几乎没有对源信号的概率密度函数做任何假设,可以对几乎所有概率密度的源信号进行分离,还借鉴了Comon的成对处理原则,把算法推广到了解决一般的盲源分离问题。仿真证明了该算法的有效性。 相似文献
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基于小波变换和盲信号分离的多通道肌电信号处理方法 总被引:5,自引:1,他引:4
为了消除多通道表面肌电信号(SEMG)采集时形成的混叠现象,提出一种新的SEMG处理方法.该方法将小波变换和独立分量分析(ICA)结合,利用小波变换的去噪作用,滤除混合在原始SEMG中的部分噪声后作为ICA的输入信号,采用Infomax算法对输入信号实施盲分离,并引入相关系数验证ICA分量与源信号的一致性.实验结果表明,该方法用于多通道SEMG的盲分离是很有效的. 相似文献
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从随机变量(微分)熵的概念出发,定义了随机变量的相似度,讨论了用求相似度极点的方法实现观测数据线性组合非高斯性最大化,从而串行估计独立分量分析(ICA)模型中的独立分量的原理和算法。对非多项式矩定理进行了更为一般化的证明,以此定理为根据阐明了以一般的非二次型光滑偶函数的数学期望近似代替相似度的可行性。给出梯度算法中的符号因子计算公式,避免了现有的相应算法中符号因子计算公式与目标函数之间的矛盾。通过与极大似然ICA方法对比,表明所定义的相似度就是在预白化条件下单个源变量的极大似然函数。 相似文献
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眼动伪差和工频干扰是临床脑电图(EEG)中常见噪声,严重影响其有用信息提取.本文尝试采用独立分量分析(Independent Component Analysis,ICA)方法分离EEG中此类噪声.通过对早老性痴呆症(Alzheimer disease,AD)患者临床EEG信号(含眼动伪差和混入工频干扰,信噪比仅0dB)作ICA分析,比较了最大熵(Infomax)和扩展最大熵(Extended Infomax)ICA算法的分离效果,证实虽然最大熵算法可以分离出眼动慢波,但难以消除工频干扰,为此需采用扩展的最大熵算法;并知ICA方法在极低信噪比时也有较好的抗干扰性,且在处理非平稳信号时有好的鲁棒性;文中还结合近似熵(approximate entropy,ApEn)分析说明利用ICA去除干扰后有助于恢复和保持原始EEG信号的非线性特征.研究结果表明ICA方法在生物医学信号处理中具有潜在的重要应用价值,值得深入研究和推广. 相似文献
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独立分量分析(ICA)作为一种有效的盲源分离技术(BSS)是信号处理领域的热点。传统的独立分量分析都要求观察信号数目大于或者等于源信号数目,然而对于脑电图(EEG)等的一些信号处理中存在的源信号数目大于观察信号数目的情况,传统的独立分量分析算法不能有效分离。该文针对源信号数目大于观察信号数目的情况,在传统的独立分量分析技术的基础上,给出了一个新的学习算法,并将新算法与传统的独立分量算法进行了比较。实验仿真结果证明该算法在给定2个混合信号的情况下能够较好地分离3个未知语音信号源,成功实现了源信号数目大于观察信号数目情况下的盲源分离。 相似文献
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This paper takes a close look at the block Toeplitz structure and block-inner diagonal structure of auto correlation matrices of source signals in convolutive blind source separation (BSS) problems. The aim is to propose a one-stage time-domain algorithm for convolutive BSS by explicitly exploiting the structure in autocorrelation matrices of source signals at different time delays and inherent relations among these matrices. The main idea behind the proposed algorithm is to implement the joint block Toeplitzation and block-inner diagonalization (JBTBID) of a set of correlation matrices of the observed vector sequence such that the mixture matrix can be extracted. For this purpose, a novel tri-quadratic cost function is introduced. The important feature of this tri-quadratic contrast function enables the development of an efficient algebraic method based on triple iterations for searching the minimum point of the cost function, which is called the triply iterative algorithm (TIA). Through the cyclic minimization process in the proposed TIA, it is expected that the JBTBID is achieved. The source signals can be retrieved. Moreover, the asymptotic convergence of the proposed TIA is analyzed. Convergence performance of the TIA and the separation results are also demonstrated by simulations in comparison with some other prominent two-stage time-domain methods. 相似文献
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In recent years, blind source separation (BSS) by independent component analysis (ICA) has been drawing much attention because of its potential applications in signal processing such as in speech recognition systems, telecommunication and medical signal processing. In this paper, two algorithms of independent component analysis (fixed-point IC,4 and natural gradient-flexible ICA) are adopted to extract human epileptic feature spikes from interferential signals. Experiment results show that epileptic spikes can be extracted from noise successfully. The kurtosis of the epileptic component signal separated is much better than that of other noisy signals. It shows that ICA is an effective tool to extract epileptic spikes from patients' electroencephalogram EEG and shows promising application to assist physicians to diagnose epilepsy and estimate the epileptogenic region in clinic. 相似文献
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Xiao-Long Zhu Xian-Da Zhang Zi-Zhe Ding Ying Jia 《IEEE transactions on circuits and systems. I, Regular papers》2006,53(3):745-753
Blind source separation (BSS) aims at recovering statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Besides independent component analysis, nonlinear principal component analysis (NPCA) is shown to be another useful tool for solving this problem, but it requires that the measured data be prewhitened. By taking into account the autocorrelation matrix of the measured data, we present in this paper a modified NPCA criterion, and develop a least-mean-square (LMS) algorithm and a recursive least-squares algorithm. They can perform the online BSS using directly the unwhitened observations. Since a natural gradient learning is applied and the prewhitening process is removed, the proposed algorithms work more efficiently than the existing NPCA algorithms, as verified by computer simulations on man-made sources as well as practical speech signals. 相似文献
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Bohm M Stadlthanner K Gruber P Theis FJ Lang EW Tome AM Teixeira AR Gronwald W Kalbitzer HR 《IEEE transactions on bio-medical engineering》2006,53(5):810-820
In this paper, an automatic assignment tool, called BSS-AutoAssign,for artifact-related decorrelated components within a second-order blind source separation (BSS) is presented. The latter is based on the recently proposed algorithm dAMUSE, which provides an elegant solution to both the BSS and the denoising problem simultaneously. BSS-AutoAssign uses a local principal component analysis (PCA)to approximate the artifact signal and defines a suitable cost function which is optimized using simulated annealing. The algorithms dAMUSE plus BSS-AutoAssign are illustrated by applying them to the separation of water artifacts from two-dimensional nuclear overhauser enhancement (2-D NOESY)spectroscopy signals of proteins dissolved in water. 相似文献
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盲信号分离的现状和展望 总被引:11,自引:0,他引:11
盲信号分离是近几年才发展起来,用于解决从混合观测数据中分离源信号的一门新技术,已在许多领域获得了广泛应用。本文介绍了盲分离的主要理论和两大类实现方法——独立分量分析和非线性主分量分析,并在此基础上介绍了实现盲信号分离的不同算法、在非线性混合情况下的算法以及盲信号分离将来的发展方向。 相似文献
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A frequently encountered problem in signal processing is harmonic retrieval in additive colored Gaussian or non-Gaussian noise,
especially when the frequencies of the harmonic signals are very close in space. The purpose of this paper is to develop an
efficient Blind Source Separation (BSS) algorithm from linear mixtures of source signals, which enables to separate harmonic
source signals using only one observed channel signal even if the frequencies of the harmonic signals are closely spaced.
First, we establish the BSS based harmonic retrieval model in additive noise by using the only one observed channel, and analyze
the fundamental principle by utilizing BSS method to retrieve harmonics. Then, we propose a BSS-based approach to the harmonic
retrieval by resorting the concept of W-disjoint orthogonality in the over-complete BSS situation, and as a result, we get
the separation algorithm using only one channel mixed signals. Simulation results show that the proposed separation algorithm-BSS-HR
is able to separate the harmonic source signals. 相似文献
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针对目前在同一热点区域内高密度部署无线接入点(AP)造成的信道干扰问题,该文结合同一个基础服务集(BSS)内终端的服务质量(QoS)和BSS间的负载均衡的需要,分析了基于IEEE 802.11e的3维离散马尔科夫链模型,并修订了其中竞争窗、退避计数器以及重传次数的量化关系,完善了该模型,得到了更为准确的基于QoS的终端归一化吞吐量表达式;其次,在信道分配设计时,既考虑了每个BSS内终端和AP之间通信的QoS,又考虑了每个AP范围内所有与之关联的终端受到相邻AP信道干扰下整体吞吐量的公平性,并对信道干扰进行了分析,将信道分配问题建模为一个最优化问题。最后,通过遗传算法获得了AP之间的最优信道分配。数值分析结果表明,基于公平和服务质量的信道分配方法CAFQ与Hsum和CAOTR算法相比,可以使BSS间的互干扰最小,BSS内基于QoS的吞吐量得到最大的保证,同时在BSS间的负载均衡方面也体现了较好的公平性。 相似文献
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Blind signal separation: statistical principles 总被引:41,自引:0,他引:41
Cardoso J.-F. 《Proceedings of the IEEE. Institute of Electrical and Electronics Engineers》1998,86(10):2009-2025
Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis that aim to recover unobserved signals or “sources” from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach, but it requires us to venture beyond familiar second order statistics, The objectives of this paper are to review some of the approaches that have been developed to address this problem, to illustrate how they stem from basic principles, and to show how they relate to each other 相似文献