一种基于非线性PCA的卷积混合盲源分离算法

 本文针对卷积混合的盲源分离,首先提出了一种基于非线性主成分分析(PCA)的准则,然后推导出了一种递推最小二乘(RLS)算法.该算法为时域盲源分离算法,分为两个阶段,第一阶段为混合信号的白化,第二阶段为基于白化信号的RLS迭代.新算法不存在频域算法在每个频点的幅度与次序不确定性的问题,具有较快的收敛速度.文中最后通过仿真试验验证了本文算法的有效性.     

基于PSO-CF的有限目标信号盲抽取算法

以最大化规范四阶累积量绝对值为目标函数,解决了因概率密度函数估计过程中激活函数选取不当而带来算法分离性能下降的问题;引入PSO-CF方法进行优化问题求解,防止算法收敛到局部极值,降低算法的实现复杂度。为高效实现从多源混合信号中抽取有限数目的目标信号,提出了一种基于PSO-CF的有限目标信号盲抽取算法。仿真表明,该算法对超高斯、亚高斯及混合型源信号均可分离,算法普适性强,且收敛速度快,分离性能良好。     

一种新型的卷积混和盲信号分离算法

本文提出了一种新的卷积混合盲信号分离算法。首先将卷积混合模型进行等价简化,再用理论推导论证了卷积混合盲分离问题等价于最优化Wiener滤波器问题,给出了一种去相关分离准则函数。最后,对两个源信号卷积混合的情况,用标准自适应LMS算法使分离准则函数达到最小值,从而得到了两个信号混合的分离算法;然后,推广到多个源信号混合得到了相应分离算法。通过计算机仿真试验验证了本算法的有效性和可行性。     

盲解卷积与窄带阵列模型结合的信号分离算法

在水声制导技术中,提出了一种高效分离算法,实现了对多目标源信号的分离,为系统后端对多目标定位提供了技术支持。窄带信号条件下,把盲分离与阵列信号处理结合起来．借助阵列模型把接收的混合信号变成解析信号,然后利用瞬时复值盲分离算法进行分离获得源信号的解析信号,取实部后便是实源信号。从而将实数的卷积混合转化为复数的瞬时混合,在盲分离阵列模型的基础上,通过复数盲分离的手段完成盲解卷积。解卷积恢复的多目标源信号十分有利于多目标特征识别与定位。通过构建正弦信号盲解卷积仿真实验,对文中提出的盲解卷积方法进行了验证。结果表明了该方法的正确性。     

独立分量分析盲信号分离方法研究

盲源分离是指从多个相互独立的源信号的混合信号中分离出源信号来。独立分量分析法是盲源分离的一种新方法,由于其在语音信号处理、阵列信号处理、生物医学信号处理、移动通信及图象处理等领域的应用前景,越来越引起人们的关注,成为研究的热点。介绍一种基于四阶累积量的非高斯性最大化的ICA算法解决盲源分离的问题,并给出了该算法分离通信信号的计算机仿真结果,验证了算法的有效性。     

基于四阶累积量和简化粒子群的盲分离算法

针对线性混合盲信号分离问题,本文提出一种基于四阶累积量和简化粒子群优化的盲信号分离新算法。该算法采用信号的四阶累积量作为目标函数,运用简化粒子群优化算法对目标函数进行优化,最终实现对混合信号的盲分离。仿真结果表明,该算法能够有效实现对会议语音混合信号的盲分离,跟其他算法相比,具有更快收敛速度和分离精度。     

卷积混叠信号的最小互信息量盲分离算法

本文提出了一种卷积混叠信号的盲分离算法。该算法利用当信号相互独立时互信息量最小的特性作为分离准则,应用随机梯度算法确定分离滤波器的系数,文中给出了详细的理论推导。理论分析及实验仿真证明了算法的有效性,并且算法对混合滤波器没有特殊限制     

非平稳卷积混合信号的盲分离

传感器采集到的信号是由多目标源、环境噪声等经多途径卷积混合的形武.为有效地去除环境因素如干扰、传输延迟等的影响,提出一种新的盲信号分离方法.利用非平稳信号的多重去相关和最小二乘准则来估计混合矩阵A或解混矩阵W以及信号和噪声功率.实验结果表明,该算法具有良好的分离效果.     

面向多路源信号的单通道盲去卷积算法研究

传统的单通道盲去卷积的方法存在仅能从混合信号中分离出2路源信号的局限,考虑到以上问题,该文提出一种基于优化的深度卷积生成对抗网络的单通道盲去卷积算法(SCBDC),能从1路混合信号中分离和解卷积出3路以上的独立源信号和混合矩阵.该文实验在汉字和遮挡图像数据集上进行,随机选择4路信号与混合矩阵进行卷积混合,实验结合峰值信...     

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。     

高斯色噪声背景下的单通道信源数目估计算法

目前信源数目估计算法大都是基于多通道接收模型且对高斯色噪声抑制能力较差,而实际应用中单通道接收模型及色噪声环境非常普遍,因此研究色噪声背景下的单通道信源数目估计算法意义重大。针对现有算法的缺陷提出了一种基于构建信号时间快拍和四阶累积量矩阵的单通道信源数目估计算法。首先通过构建信号时间快拍实现单通道接收信号的升维得到矢量化空间,然后以此组信号空间构造出四阶累积量矩阵,并从理论上验证了该四阶累积量矩阵能有效抑制高斯白噪声及高斯色噪声的影响,最后对该矩阵进行奇异值分解并通过信息论准则估计出信源个数。仿真实验和实际信号实验都表明本文算法能较好地解决单通道信源数目估计问题,且能有效抑制高斯色噪声。      

Image motion estimation algorithms using cumulants

A class of algorithms is presented that estimates the displacement vector from two successive image frames consisting of signal plus noise. In the model, the signals are assumed to be either non-Gaussian or (quasistationary) deterministic; and, via a consistency result for cumulant estimators, the authors unify the stochastic and deterministic signal viewpoints. The noise sources are assumed to be Gaussian (perhaps spatially and temporally correlated) and of unknown covariance. Viewing image motion estimation as a 2D time delay estimation problem, the displacement vector of a moving object is estimated by solving linear equations involving third-order auto-cumulants and cross-cumulants. Additionally, a block-matching algorithm is developed that follows from a cumulant-error optimality criterion. Finally, the displacement vector for each pel is estimated using a recursive algorithm that minimizes a mean 2D fourth-order cumulant criterion. Simulation results are presented and discussed.     

一种语音与背景乐音信号盲分离优化算法

在盲分离算法中,许多智能优化算法被应用,以克服独立性准则函数的优化进入局部最优位置,但这些优化算法的性能依赖控制参数的选择。因此,提出利用一种单参数的纯随机搜索的单形进化优化算法(Surface-Simplex Swarm Evolution,SSSE),克服算法参数对优化算法性能的影响,提高盲分离算法的应用有效性,并将该改进盲分离算法应用于语音与背景乐音信号的盲分离。实验中,以四阶累积量作为独立分量分析(Independent Component Analysis,ICA)中的准则函数。实验结果表明,该改进算法有效分离出语音成分与背景乐音成分,而且在稳定性和分离效果方面具有较好的性能。     

Matrix-Group Algorithm via Improved Whitening Process for Extracting Statistically Independent Sources From Array Signals

This paper addresses the problem of blind separation of multiple independent sources from observed array output signals. The main contributions in this paper include an improved whitening scheme for estimation of signal subspace, a novel biquadratic contrast function for extraction of independent sources, and an efficient alterative method for joint implementation of a set of approximate diagonalization-structural matrices. Specifically, an improved whitening scheme is first developed by estimating the signal subspace jointly from a set of diagonalization-structural matrices based on the proposed cyclic maximizer of an interesting cost function. Moreover, the globally asymptotical convergence of the proposed cyclic maximizer is analyzed and proved. Next, a novel biquadratic contrast function is proposed for extracting one single independent component from a slice matrix group of any order cumulant of the array signals in the presence of temporally white noise. A fast fixed-point algorithm that is a cyclic minimizer is constructed for searching a minimum point of the proposed contrast function. The globally asymptotical convergence of the proposed fixed-point algorithm is analyzed. Then, multiple independent components are obtained by using repeatedly the proposed fixed-point algorithm for extracting one single independent component, and the orthogonality among them is achieved by the well-known QR factorization. The performance of the proposed algorithms is illustrated by simulation results and is compared with three related blind source separation algorithms     

Joint blind source separation by generalized joint diagonalization of cumulant matrices

In this paper, we show that the joint blind source separation (JBSS) problem can be solved by jointly diagonalizing cumulant matrices of any order higher than one, including the correlation matrices and the fourth-order cumulant matrices. We introduce an efficient iterative generalized joint diagonalization algorithm such that a series of orthogonal procrustes problems are solved. We present simulation results to show that the new algorithms can reliably solve the permutation ambiguity in JBSS and that they offer superior performance compared with existing multiset canonical correlation analysis (MCCA) and independent vector analysis (IVA) approaches. Experiment on real-world data for separation of fetal heartbeat in electrocardiogram (ECG) data demonstrates a new application of JBSS, and the success of the new algorithms for a real-world problem.     

基于四阶累积量张量联合对角化的多数据集联合盲源分离

该文提出一种基于四阶累积量张量联合对角化的联合盲源分离(J-BSS)算法。首先通过计算4阶互累积量将多数据集信号的J-BSS问题转化为4阶张量联合对角化问题。接下来,基于雅可比连续旋转将张量联合对角化这类非线性优化问题,转化为一系列可获取闭式解的简单子优化问题,并通过交替迭代对多数据集混合矩阵进行更新,进而实现J-BSS。实验结果表明,所提算法具有良好的收敛性能,较之现有的同类型BSS及J-BSS算法具有更高的精度。此外,该算法在分离实际胎儿心电信号方面也表现出良好的性能。     

基于细菌趋药性算法的盲图像分离方法

提出了一种新的基于细菌趋药性(BC)算法的盲图像分离方法,利用图像信号的规范四阶累积量作为目标函数,使用BC算法对目标函数进行优化以实现图像的盲分离。每分离出一幅图像后,从混合图像中消除该幅图像成分后再进行下一次分离,从而最终实现所有源图像的逐次分离。仿真结果表明,本文算法能够有效实现对多幅混合自然图像的盲分离,且具有较好的分离效果。     

一种基于盲源分离的雷达信号分选方法

盲信号分离是近几年才发展起来的,用于解决从混合观测数据中分离源信号的一门新技术,已在众多领域中获得了广泛的应用。文中结合高阶累积量对高斯噪声的不敏感性,给出了一种基于盲源分离的雷达信号分选方法,可以对高斯噪声背景中混合的多个雷达信号实现分选。最后给出了该方法的计算机仿真,结果证明了该方法的有效性。     

基于四阶累积量的EM波达方向估计算法

针对低信噪比（signal-to-noise ratio,SNR）下,经典波达方向估计性能下降的问题,提出将信号的四阶累积量与期望最大化（expectation maximization,EM）算法相结合的波达方向估计算法.该方法引入隐含变量进行更新迭代,并求隐含变量的四阶累积量,构造关于待估波达方向的极大似然函数从而求解出信号的波达方向角.仿真结果表明:本文算法能有效地抑制高斯噪声对信号参数估计的影响,同时能利用迭代来提高估计精度.在低SNR时其估计性能优良,具有很好的稳定性和分辨率,有利于高分辨地估计信号的波达方向.     

Passive Localization of Mixed Near-Field and Far-Field Sources Using Two-stage MUSIC Algorithm

Passive source localization is one of the issues in array signal processing fields. In some practical applications, the signals received by an array are the mixture of near-field and far-field sources, such as speaker localization using microphone arrays and guidance (homing) systems. To localize mixed near-field and far-field sources, this paper develops a two-stage MUSIC algorithm using cumulant. The key points of this paper are: (i) in the first stage, this paper derives one special cumulant matrix, in which the virtual ?steering vector? is the function of the common electric angle in both near-field and far-field signal models so that source direction-of-arrival (DOA) (near-field or far-field one) can be obtained from this electric angle using the conventional high-resolution MUSIC algorithm; (ii) in the second stage, this paper derives another particular cumulant matrix, in which the virtual ?steering matrix? has full column rank no matter whether the received signals are multiple near-field sources or multiple far-field ones or their mixture. What is more important, the virtual ?steering vector? can be separated into two parts, in which the first one is the function of the common electric angle in both signal models, whereas the second part is the function of the electric angle that exists only in near-field signal model. Furthermore, by substituting the common electric angle estimated in the first stage into one special Hermitian matrix formed from another MUSIC spectral function, the range of near-field sources can be obtained from the eigenvector of the Hermitian matrix. The resultant algorithm avoids two- dimensional search and pairing parameters; in addition, it avoids the estimation failure problem and alleviates aperture loss. Simulation results are presented to validate the performance of the proposed method.