基于时频域单源区域的延迟欠定混合非平稳信号盲分离

针对衰减-延迟欠定混合信号的盲分离问题,提出了基于子空间分解的时频域上单源区域检测方法,估计出信号在时频域上的单源区域以及相应的特征向量,然后利用系统聚类法对单源区域对应的特征向量进行聚类分析,估计出源信号数目以及混合矩阵,最后通过改进的基于子空间投影算法完成源信号的恢复.仿真结果表明本文算法提高了混合矩阵和源信号的估...     

基于谐波提取的欠定语音盲分离方法

现有的欠定语音信号盲分离算法往往不能同时兼顾分离性能及效率。针对此问题,本文提出一种基于谐波提取的欠定盲分离方法。首先,利用频谱校正从混合信号的短时傅立叶变换中提取谐波参数,其次利用相位一致性准则甄别这些参数的单源属性,进而用自适应K-均值方法对单源模式做聚类而获得源数估计和混合矩阵估计,最后再用子空间投影法恢复源信号。其中谐波提取和单源参数筛选可保证低复杂度地精确估计出混合矩阵。仿真实验表明,相比于原始子空间投影算法,本文方法可获得更高的信号恢复质量,且在谐波相关领域也具有潜在应用价值。      

一种充分利用变量结构的解卷积 混合盲源分离新方法

 针对卷积混合盲源分离问题,提出一种基于接收信号不同延时下自相关矩阵组的联合块内对角化方法.为了求解表征联合块内对角化近似程度的基于最小二乘的三二次代价函数,给出基于梯度下降法的三迭代算法.该算法在充分利用混迭矩阵的块Toeplitz结构和源信号相关矩阵的块内对角化结构的基础上,交替估计代价函数中的三组待定参数,搜索代价函数最小点,从而得到混迭矩阵的估计,实现信道的盲均衡和源信号的盲分离.分析了三迭代算法的收敛性能,证明即使存在估计误差时,该算法依然全局渐进收敛.仿真结果表明,与其他经典的两步算法相比,提出的一步算法能够更好地估计混迭矩阵并恢复出源信号,有效地解决了卷积混合盲源分离问题.     

有色信源卷积混合盲源分离算法

由于缺乏有色信源的时域相关性等先验信息,在设计有色信源卷积混合盲分离算法时难以利用源信号的特性,造成“有色源”的卷积混合盲分离比“白色源”的卷积混合盲分离更具挑战性。通过挖掘信源有色特性对卷积混合多项式矩阵的作用机制,提出了一种新的有色信源卷积混合盲源分离算法。首先将有色信源建模为白色信源激励有限冲击响应（Finite Impulse Response,FIR）滤波器的响应,将有色信源卷积混合模型转化为等效的白色源卷积混合模型,此时等效的卷积混合多项式矩阵即为FIR滤波器和原混合多项式矩阵的乘积;其次使用基于白化的方法获得白色激励源和等效卷积混合多项式矩阵的估计;最后利用最大公因式提取方法从等效卷积混合多项式矩阵中提取岀FIR滤波器,进而恢复出原始的有色信源。仿真结果表明,所提算法在不同信噪比下的分离性能均优于现有算法,具有良好的实用性。     

基于时频分布的欠定混叠盲分离

针对欠定混合信号的盲分离问题,提出了基于时频分布的欠定盲分离算法,首先计算信号的时频分布矩阵并找出信号的自源时频点,然后把自源点对应的时频分布矩阵表示成三阶张量并通过张量分解估计出混合矩阵,最后通过计算矩阵的伪逆和时频合成来完成源信号的恢复.该算法不需要假设源信号是稀疏的或相互独立的.仿真结果表明与已有算法相比本文方法...     

脉冲噪声中的盲源分离方法

盲源分离的目的在于只利用接收数据把被瞬时线性混合的源信号恢复出来,该文讨论的是一种在复各向同性的SS噪声中的盲源分离方法,SS过程能够很好地描述许多具有冲激特性的信号和噪声,但其二阶和高阶统计量是不存在的,所以首先用基于子空间逼近和白化的方法对观测数据进行处理,然后利用特征矩阵近似联合对角化方法来估计源信号和混合矩阵。仿真结果说明该方法具有良好的性能。     

一种欠定盲源分离中混合矩阵的估计方法

欠定盲源分离问题中基于源信号稀疏性的两阶段法中,混合矩阵估计的准确与否,直接影响源信号的恢复效果。文中提出了一种在稀疏域估计混合矩阵的新方法。该方法通过搜索稀疏域中同一直线附近的点,利用这些点重构出混合矩阵,避免了远离直线周边的点对估计混合矩阵的干扰,从而大大降低了计算量。仿真表明该算法性能良好。     

基于比率矩阵聚类的欠定盲分离算法

利用稀疏分量的直线聚类性,提出了欠定盲源分离中估计混合矩阵的一种方法。该方法通过构造比率矩阵对观测信号进行分选,剔除了源信号频谱重叠的部分,然后利用鲁棒竞争的聚类学习算法获得对混合矩阵的精确估计,解决了源信号在频域不充分稀疏的条件下准确估计混合矩阵的问题。在估计出混合矩阵的基础上,利用最短路径法分离出源信号。由仿真结果可以看出,与传统的K均值估计混合矩阵的方法相比,方法具有更好的鲁棒性。     

基于约束NMF的盲源分离算法

非负矩阵分解(NMF)是解决非独立源信号混合的盲分离的另一条新途径.该文提出一种基于约束NMF的盲源分离算法,在对NMF估计得到的源信号施加最小相关约束的基础上,对混合矩阵估计施加行列式约束,实现NMF的唯一分解.与已有算法相比,本算法放宽了对混合矩阵的稀疏性要求,大幅提高了信号分离质量.该算法仍适用于独立源信号分离问题.     

基于两步法稀疏分量分析的欠定盲源分离

分析了解决欠定盲源分离问题的稀疏分量分析方法。首先讨论了数据矩阵稀疏表示（分解）的方法,其次重点讨论了基于稀疏因式分解方法的盲源分离。该盲源分离技术分两步．一步是估计混合矩阵,第二步是估计源矩阵。如源信号是高度稀疏的,盲分离可直接在时域内实现。否则．对观测的混合矩阵运用小波包变换预处理后才能进行。仿真结果证明了理论分析的正确性。     

A novel signal separation algorithm based on compressed sensing for wideband spectrum sensing in cognitive radio networks

In cognitive radio networks, since cognitive terminals use a shared wideband frequency spectrum for data transmissions, they are susceptible to malicious denial‐of‐service attacks, where adversaries try to corrupt communication by actively transmitting interference signals. To address this issue, in this paper, we propose a novel signal separation algorithm based on compressed sensing, which can not only recover the entire spectrum but also separate mixed occupying signals. Specifically, the proposed algorithm is executed following three steps: (i) each cognitive terminal attempts to recover all signals over an entire wideband spectrum employing the compressed sensing technique; (ii) all cognitive terminals send their recovered signals to the fusion center where a wavelet edge detection method is adopted to locate the spectrum edges of these signals and then divide the entire spectrum into several sub‐bands; (iii) the fusion center separates its received signals on each spectrum sub‐band into different categories according to their features. Both analytical and simulation results indicate that this novel compressed‐sensing‐based algorithm can effectively separate wideband signals at a low cost and combat interference of the malicious terminals in cognitive radio networks as well. Copyright © 2013 John Wiley & Sons, Ltd.     

Adaptive blind source separation for virtually any sourceprobability density function

Blind source separation (BSS) aims to recover a set of statistically independent source signals from a set of linear mixtures of the same sources. In the noiseless real-mixture two-source two-sensor scenario, once the observations are whitened (decorrelated and normalized), only a Givens rotation matrix remains to be identified in order to achieve the source separation. In this paper an adaptive estimator of the angle that characterizes such a rotation is derived. It is shown to converge to a stable valid separation solution with the only condition that the sum of source kurtosis be distinct from zero. An asymptotic performance analysis is carried out, resulting in a closed-form expression for the asymptotic probability density function of the proposed estimator. It is shown how the estimator can be incorporated into a complete adaptive source separation system by combining it with an adaptive prewhitening strategy and how it can be useful in a general BSS scenario of more than two signals by means of a pairwise approach. A variety of simulations assess the accuracy of the asymptotic results, display the properties of the estimator (such as its robust fast convergence), and compare this on-line BSS implementation with other adaptive BSS procedures     

基于三阶统计量的欠定盲源分离方法

盲源分离(BSS)在缺失源信号信息及信息混合方式信息的情况下,仅利用观测信号实现源信号恢复,是信号处理中的重要手段。欠定盲源分离(UBSS)中观测信号少于源信号数目,因此,相较于正定/超定情形,其更接近现实情况。然而,观测信号往往受到噪声干扰,传统基于2阶统计量和信号稀疏性的欠定盲源分离结果对噪声较为敏感。鉴于3阶统计量在处理对称分布噪声时的优势,该文利用观测信号的3阶统计信息实现混合矩阵的估计。考虑到源信号的自相关特性,计算多时延下观测信号一系列的3阶统计信息,并堆叠成4阶张量,进而将混合矩阵估计问题转化为4阶张量的典范双峰分解问题。该文进一步利用广义高斯模型和期望最大算法实现源信号的恢复。1000次蒙特卡罗实验表明该文算法能够有效抑制噪声的影响。针对3×4混合模型,当信噪比为15 dB时,该文算法对混合矩阵的平均估计误差达到–20.35 dB,所恢复出的源信号与真实源信号之间的平均绝对相关系数达0.84,与现有方法相比,取得了最好的分离结果。     

盲信号分离

阵列处理和数据分析的一个典型问题是从混合的观测数据向量中恢复不可观测的各个源信号.盲信号分离是解决这一问题的一门新技术,近几年吸引了信号处理学界和神经网络学界众多学者的研究兴趣.本文将以独立分量分析和非线性主分量分析为主要对象,综述盲信号分离技术的理论、方法及应用等方面的发展,并作有关展望.     

Harmonic Signals Retrieval Approach Based on Blind Source Separation

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.     

A blind source separation based method for speech encryption

The underdetermined problem poses a significant challenge in blind source separation (BSS) where the number of the source signals is greater than that of the mixed signals. Motivated by the fact that the security of many cryptosystems relies on the apparent intractability of the computational problems such as the integer factorization problem, we exploit the intractability of the underdetermined BSS problem to present a novel BSS-based speech encryption by properly constructing the underdetermined mixing matrix for encryption, and by generating the key signals that satisfy the necessary condition for the proposed method to be unconditionally secure. Both extensive computer simulations and performance analyses results show that the proposed method has high level of security while retaining excellent audio quality.     

Blind signal separation of linear mixture using trilinear decomposition

This paper introduces a new source separation technique exploiting the time coherence of the source signals. The proposed approach relies only on stationary second order statistics. Blind Signal Separation (BSS) method using trilinear decomposition is proposed in this paper. Simulation results reveal that our proposed algorithm has the better blind signal separation performance than joint di-agonalization method. Our proposed algorithm does not require whitening processing. Moreover, our proposed algorithm works well in the underdetermined condition, where the number of sources exceeds than the number of sensors.     

基于改进人工蜂群算法的盲源分离方法

 针对现有盲源分离方法大多存在收敛速度慢、分离精度低的问题,提出一种基于改进人工蜂群(Artificial Bee Colony,ABC)算法的盲信号分离方法.在ABC的邻域搜索公式中自适应调整步长,并加入全局最优解指导项,增强局部趋化性搜索能力.改进的ABC算法保持了ABC全局搜索和局部搜索之间的平衡,使ABC算法可以达到更好的寻优效果,从而提高盲源分离算法的分离精度和稳定性.实验结果表明,提出的改进盲源分离算法可以有效地分离线性瞬时混合信号.与其它算法相比,该算法具有更优异的分离性能,并具有更快的收敛速度.     

Blind separation of linear instantaneous mixtures of nonstationary surface myoelectric signals

Electromyographic (EMG) recordings detected over the skin may be mixtures of signals generated by different active muscles due to the phenomena related to volume conduction. Separation of the sources is necessary when single muscle activity has to be detected. Signals generated by different muscles may be considered uncorrelated but in general overlap in time and frequency. Under certain assumptions, mixtures of surface EMG signals can be considered as linear instantaneous but no a priori information about the mixing matrix is available when different muscles are active. In this study, we applied blind source separation (BSS) methods to separate the signals generated by two active muscles during a force-varying task. As the signals are non stationary, an algorithm based on spatial time-frequency distributions was applied on simulated and experimental EMG signals. The experimental signals were collected from the flexor carpi radialis and the pronator teres muscles which could be activated selectively for wrist flexion and rotation, respectively. From the simulations, correlation coefficients between the reference and reconstructed sources were higher than 0.85 for signals largely overlapping both in time and frequency and for signal-to-noise ratios as low as 5 dB. The Choi-Williams and Bessel kernels, in this case, performed better than the Wigner-Ville one. Moreover, the selection of time-frequency points for the procedure of joint diagonalization used in the BSS algorithm significantly influenced the results. For the experimental signals, the interference of the other source in each reconstructed source was significantly attenuated by the application of the BSS method. The ratio between root-mean-square values of the signals from the two sources detected over one of the muscles increased from (mean +/- standard deviation) 2.33 +/- 1.04 to 4.51 +/- 1.37 and from 1.55 +/- 0.46 to 2.72 +/- 0.65 for wrist flexion and rotation, respectively. This increment was statistically significant. It was concluded that the BSS approach applied is promising for the separation of surface EMG signals, with applications ranging from muscle assessment to detection of muscle activation intervals, and to the control of myoelectric prostheses.     

通信中的盲源分离问题及解决方案探讨

主要阐述了盲源分离技术应用于无线通信系统时面临的若干问题和解决方案的研究现状。在无线通信系统中,参与混合的源信号是射频通信信号和干扰,这些源信号往往具有循环平稳特性;完成源的混合的无线信道往往受环境影响而发生变化,即具有时变特性;受客观实际条件的限制,观测信号数目往往少于混合源的数目,即表现为欠定混合特性。因此,将盲源分离理论应用于无线通信系统中时,需要特别关注的是循环平稳源、时变、欠定等条件下的盲分离问题。