一种任意信号源盲分离的高效算法

提出了信号源盲分离的DBBSS算法.利用随机变量概率密度函数非参数估计的核函数法,对混合信号的概率密度函数及其导数进行估计,并由此估计信号的评价函数(score function).解决了现有信号源盲分离算法中,普遍存在的非线性函数只能凭经验选取,以及混合信号同时包含超高斯信号和亚高斯信号时,算法失效的问题.该方法非常简单,可以直接应用于所有以非线性函数代替评价函数的信号源盲分离算法.仿真结果验证了算法的有效性.     

一类基于非线性PCA准则的复数信号盲分离算法

在阵列信号处理过程中，经常遇到复数信号盲分离问题。例如，卷积混合型的源信号的盲分离；声纳信号盲分离。本文提出了一类基于非线性准则的复数信号盲分离算法。将非线性函数引入学习过程，由算法自动调节学习速率。计算机仿真实验验证了算法的有效性，文中给出了验证结果。     

基于峭度的盲分离在通信信号盲侦察中的应用

为实现复杂多信号环境下的通信信号侦察,采用一种新的盲侦察技术,即运用盲源分离算法,在没有任何先验知识的情况下分离出源信号,然后对分离的各个信号进行后续处理。提出一种改进的基于峭度的盲分离算法,可以自适应地确定激活函数。将其应用在通信信号盲侦察中,可以实现对任意源信号进行盲分离,而不管它是超高斯还是亚高斯信号。选择超高斯和亚高斯混合通信信号进行了仿真实验,结果验证了该算法的有效性。     

基于遗传算法的超亚高斯混合信号盲分离

针对基于扩展信息最大化算法的盲源分离算法在分离超亚高斯混合信号时依赖于信号的峭度估计且对初始分离矩阵和步长较为敏感的问题,提出了一种基于遗传算法的盲源分离算法。该算法以分离信号之间的互信息作为代价函数,采用非多项式函数的逼近方法解决了互信息求解过程中涉及到的负熵的计算问题,用遗传算法代替梯度寻优算法最小化代价函数。仿真结果表明:在分离超亚高斯混合信号时,该算法计算简单,鲁棒性好,迭代100次时性能指数值达到0.025 5,分离性能优于基于扩展信息最大化算法的盲源分离算法。     

未知源信号峭度正负性的盲分离

大多数的盲分离算法假设源信号峭度的正负性是己知的，并据此选择相应的非线性函数近似评价函数(score function)。针对源信号峭度的正负性未知的情况，本文提出了一个评价函数的参数估计方法，本算法能有效地分离混合在一起的超高斯信号和亚高斯信号，仿真结果验证了算法的有效性。     

基于Borel测度峰值判定的欠定混合盲语音信号分离

简要介绍稳定分布的特征函数及其Borel测度表示,给出了Borel测度的估计方法,并利用Borel测度的峰值确定混合矩阵的基矢量,从而可以确定各个独立分量,实现信号的盲分离。计算机模拟和分析表明,这种算法是一种在高斯和分数低阶Alpha稳定分布噪声条件下具有良好韧性的独立分量分析与盲源分离方法,在盲语音混合信号的分离应用中也得到了很好的效果。     

盲信号分离的现状和展望

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。     

基于非参数密度估计的多通道盲解卷积

非参数密度估计方法被用来直接估计在自然梯度盲解郑积算法中遇到的评价函数(score function)。与用一个非线性函数简单地代替评价函数相比较,这种直接估计评价函数的方法的主要优点是:它可以用来对杂系混合信号,即同时包含超高斯和亚高斯的信号,进行盲解卷积。因为评价函数可以被直接的估计出来,因此,就不需要针对不同的源信号选择不同的非线性函数来代替评价函数。这种方法可以用在更加“盲”的情况。     

带噪的战场声信号盲分离方法研究

提出了一种噪声环境中战场混合声信号盲分离方法.基于含噪的独立分量分析模型,对观测信号进行准白化,去除噪声引起的协方差偏移量;定义观测信号中随机变量的高斯矩为无偏估计的目标函数,最大化此目标函数得到了一种改进的FastICA算法,应用于带噪的战场混合声信号盲分离.仿真实验证明,改进算法能较好改善分离效果,具有很好的鲁棒性.     

基于非高斯性和广义复杂度寻踪的盲源分离算法

现有的盲源分离算法往往利用信号某一方面的统计特性来分离信号,例如:利用信号的非高斯特性,或者利用信号的时序特性.在实际应用中,信号往往是具有这两种特性信号的混合,采用信号某一方面的特性往往不能够成功的分离出信号.当源信号具有非高斯性和非线性预测特性时,提出了基于非高斯性和广义复杂度寻踪的目标函数,最小化该目标函数,提出了一个梯度下降的盲源分离算法.计算机仿真表明了提出算法的有效性,和现有的盲源分离算法相比,提出算法具有更好的信号分离性能.     

基于极坐标域主成分分析的无线通信立体声参数编码技术(英文)

High efficiency audio compression is the basic technology in audio involved multimedia communications. Downmixing and parametric coding is efficient coding scheme with wide applications in some up-to-date audio codecs such as Parametric Stereo (PS) in EAAC+ and MPEG-Surround. Principle Component Analysis (PCA) stereo coding followed this idea to map two channels to one channel with maximum energy and parameterize the secondary channel. This paper investigates the conventional PCA method performance under general stereo model with multiple sound sources and different directions, and then proposes a Polar Coordinate based PCA (PC-PCA) stereo coding method. It has been proved that when multiple sound sources exist with different directions, PC-PCA is better than the conventional PCA method when Mean to Standard deviation Ratio (MSR) is large. A stereo codec based on PC-PCA is proposed to validate the performance improvement of proposed method. Objective and subjective tests show the proposed method achieves a comparative quality and saves 50% parameter bit rate comparing with conventional PCA method, and obtains a 4-8 MUSHRA scores improvement comparing with state-of-the-art stereo codec at the same parameter bit rate.     

FUZZY PRINCIPAL COMPONENT ANALYSIS AND ITS KERNEL- BASED MODEL

Principal Component Analysis(PCA)is one of the most important feature extraction methods,and Kernel Principal Component Analysis(KPCA)is a nonlinear extension of PCA based on kernel methods.In real world,each input data may not be fully assigned to one class and it may partially belong to other classes.Based on the theory of fuzzy sets,this paper presents Fuzzy Principal Component Analysis(FPCA)and its nonlinear extension model,i.e.,Kernel-based Fuzzy Principal Component Analysis(KFPCA).The experimental results indicate that the proposed algorithms have good performances.     

基于EK算法改进的多目标跟踪技术

在随机有限集理论框架下,传统的3种基于高斯混合实现的滤波算法仅适用于线性高斯系统中的目标跟踪。为了将算法的使用范围扩展到非线性高斯系统,文中提出了传统3种滤波器基于扩展卡尔曼（EK）改进后的算法,通过泰勒级数展开将非线性问题转化为线性问题近似处理,并对比分析了3种新算法对于匀速转弯目标的跟踪性能。仿真结果表明,3种新算法均能实现对于非线性高斯系统中目标的有效跟踪。     

Nonlinear Dynamic Process Monitoring Using Canonical Variate Analysis and Kernel Density Estimations

The Principal Component Analysis (PCA) and the Partial Least Squares (PLS) are two commonly used techniques for process monitoring. Both PCA and PLS assume that the data to be analysed are not self-correlated i.e. time-independent. However, most industrial processes are dynamic so that the assumption of time-independence made by the PCA and the PLS is invalid in nature. Dynamic extensions to PCA and PLS, so called DPCA and DPLS, have been developed to address this problem, however, unsatisfactorily. Nevertheless, the Canonical Variate Analysis (CVA) is a state-space-based monitoring tool, hence is more suitable for dynamic monitoring than DPCA and DPLS. The CVA is a linear tool and traditionally for simplicity, the upper control limit (UCL) of monitoring metrics associated with the CVA is derived based on a Gaussian assumption. However, most industrial processes are nonlinear and the Gaussian assumption is invalid for such processes so that CVA with a UCL based on this assumption may not be able to correctly identify underlying faults. In this work, a new monitoring technique using the CVA with UCLs derived from the estimated probability density function through kernel density estimations (KDEs) is proposed and applied to the simulated nonlinear Tennessee Eastman Process Plant. The proposed CVA with KDE approach is able to significantly improve the monitoring performance and detect faults earlier when compared to other methods also examined in this study.      

文本无关说话人识别中一种改进的模型PCA变换方法

对于采用高斯混合模型(GMM)的与文本无关的说话人识别,出于模型参数数量和计算量的考虑 GMM的协方差矩阵通常取为对角矩阵形式,并假设观察矢量各维之间是不相关的。然而,这种假设在大多情况下是不成立的。为了使观察矢量空间适合于采用对角协方差的GMM进行拟合,通常采用对参数空间或模型空间进行解相关变换。该文提出了一种改进模型空间解相关的PCA方法,通过直接对GMM的各高斯成分的协方差进行主成分分析,使参数空间分布更符合使用对角化协方差的混合高斯分布,并通过共享PCA变换阵的方法减少参数数量和计算量。在微软语音库上的说话人识别实验表明,该方法取得了比常规的对角协方差GMM系统的最优结果有相对35%的误识率下降。     

A digital technique to estimate second-order distortion usinghigher order coherence spectra

A digital spectral method for evaluating second-order distortion of a nonlinear system, which can be represented by Volterra kernels up to second order and which is subjected to a random noise input, is discussed. The importance of departures from the commonly assumed Gaussian excitation is investigated. The Hinich test is shown to be an appropriate test for orthogonality in the system identification. Tests for Gaussianity of two important sources, which are commonly used for Gaussian inputs in nonlinear system identification, are presented: (1) commercial software routines for simulation experiments, and (2) noise generators for practical experiments. The deleterious effects of assuming a Gaussian input when it is not are demonstrated. The random input method for evaluating the second-order distortion of a nonlinear system is compared with the sine-wave input method using both simulation and experimental data. The approach is applied to a loudspeaker in the low-frequency band     

基于改进二维主成分分析的在线掌纹识别

掌纹识别是生物特征识别技术的新热点,论文提出使用二维主成分分析算法(2D PCA)提取掌纹图像的统计特征,实验表明其泛化能力优于传统主成分分析算法(PCA).在此基础上,论文提出且定义了改进的二维主成分分析,并证明它在保持训练样本图像总体散度的同时更有效的提取样本特征.改进的算法在得到99.72%高识别率的同时,大幅降低了原算法的特征维数、识别计算的复杂度,使系统的实用性进一步提高.     

基于主成分分析和字典学习的高光谱遥感图像去噪方法

高光谱图像变换域各波段图像噪声强度不同,并具有独特的结构。针对这些特点,该文提出一种基于主成分分析(Principal Component Analysis, PCA)和字典学习的高光谱遥感图像去噪新方法。首先,对高光谱数据进行PCA变换得到一组主成分图像;然后,对信息量较小的主成分图像分别采用基于自适应字典的稀疏表示方法和对偶树复小波变换方法去除空间维和光谱维的噪声;最后,通过PCA逆变换得出去噪后的数据。结合主成分分析和字典学习的优势,该文方法相对于传统方法对高光谱图像具有更好的自适应性,在细节得到保留的同时有效地抑制了斑块效应。对模拟和实际高光谱遥感图像的实验结果验证了该文方法的有效性。     

Gabor-based kernel PCA with doubly nonlinear mapping for face recognition with a single face image.

In this paper, a novel Gabor-based kernel principal component analysis (PCA) with doubly nonlinear mapping is proposed for human face recognition. In our approach, the Gabor wavelets are used to extract facial features, then a doubly nonlinear mapping kernel PCA (DKPCA) is proposed to perform feature transformation and face recognition. The conventional kernel PCA nonlinearly maps an input image into a high-dimensional feature space in order to make the mapped features linearly separable. However, this method does not consider the structural characteristics of the face images, and it is difficult to determine which nonlinear mapping is more effective for face recognition. In this paper, a new method of nonlinear mapping, which is performed in the original feature space, is defined. The proposed nonlinear mapping not only considers the statistical property of the input features, but also adopts an eigenmask to emphasize those important facial feature points. Therefore, after this mapping, the transformed features have a higher discriminating power, and the relative importance of the features adapts to the spatial importance of the face images. This new nonlinear mapping is combined with the conventional kernel PCA to be called "doubly" nonlinear mapping kernel PCA. The proposed algorithm is evaluated based on the Yale database, the AR database, the ORL database and the YaleB database by using different face recognition methods such as PCA, Gabor wavelets plus PCA, and Gabor wavelets plus kernel PCA with fractional power polynomial models. Experiments show that consistent and promising results are obtained.     

基于主成分分析和人工神经网络的五味子质量鉴定方法研究

提出了一种采用近红外光谱技术结合人工神经网络对中药五味子质量进行鉴别的新方法.利用近红外光谱仪获得了3种不同来源地五味子合计90个样本的光谱曲线,采用主成分分析法对光谱数据进行了聚类分析,并结合人工神经网络技术建立了五味子甲素、五味子乙素和五味子醇甲三种木脂素类化合物的分析模型.主成分分析表明,前5个主成分的累积贡献率为98.75%,具有很好的聚类作用.在主成分分析的基础上,取前5个主成分的18个吸收峰作为网络的输入节点,取3项指标作为输出节点,建立了一个18(输入节点)-10(隐含层节点)-3(输出节点)的三层人工神经网络模型.五味子甲素、五味子乙素和五味子醇甲三项指标的人工神经网络模型预测值的平均相对误差分别为4.07%、2.65%和6.15%,与高效液相色谱法测定值的符合程度很高.该模型具有很好的预测能力,可用于大批量五味子的质量检测和五味子生产加工过程中的质量控制.