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。     

基于改进谱聚类的雷达信号欠定盲源分离算法

现有的模糊聚类算法在初始值选择、低收敛速度和局部极值方面存在问题,导致混合矩阵估计的低精度。针对现有雷达侦察算法抗干扰性能不高的问题,提出了一种基于谱聚类的新型两阶段欠定盲源分离(UBSS)算法。基于谱聚类的混合矩阵估计算法,在谱聚类的基础上对得到的特征矩阵进行张量分解估计出混合矩阵。在未知先验信息条件下,采用两阶段方法实现雷达信号的分离。仿真结果表明,该方法混合矩阵的估计性能优于传统方法,在未知先验信息的实际情况下恢复效果更佳。     

稀疏分量分析在欠定盲源分离问题中的研究进展及应用

伴随着国内外对盲源分离问题研究的日益深入,在独立分量分析等经典算法之外逐步发展出了许多新的算法.稀疏分量分析就是其中有效的方法之一,它利用信号的稀疏分解,克服了独立分量分析非欠定性的要求,解决了欠定情况下的盲源分离问题.本文将以稀疏分量分析为主要对象,归纳总结了近期的研究进展.     

基于ICA的雷达信号欠定盲分离算法

该文针对源信号时域和频域不充分稀疏的情况,提出了欠定盲源分离中估计混合矩阵的一种新方法。该方法对等间隔分段的观测信号应用独立分量分析(ICA)的盲分离算法获得多个子混合矩阵,然后对其分选剔除了不属于原混合矩阵的元素,最后利用C均值聚类的学习算法获得对混合矩阵的精确估计,解决了源信号在时域和频域不充分稀疏的情况下准确估计混合矩阵的问题。在估计出混合矩阵的基础上,利用基于稀疏分解的统计量算法分离出源信号。由仿真结果,以及与传统的K均值聚类,时域检索平均算法对比的实验结果说明了该文算法的有效性和鲁棒性。     

弱稀疏语音信号的欠定盲分离

为解决弱稀疏语音信号的欠定盲分离问题,根据语音信号的部分W-分离正交性,提出一种基于单源主导区间的混合矩阵盲估计方法。该方法根据单源主导区间的性质,通过二元行矢量提取单源观测样本,对单源观测样本进行K均值聚类和主成分分析来估计混合矩阵。仿真结果表明,提出的方法可有效提高分离语音的性能,与直接利用K-PCA方法相比,分离语音的平均信噪比提高了10 dB左右。     

基于源信号数目估计的欠定盲分离

该文利用欠定盲分离下稀疏源信号的特点,估计源信号的数目且恢复源信号。通常在用两步法来解决欠定盲分离时,首先利用K-均值算法对观测信号聚类估计出混叠矩阵,最后利用最短路径法来恢复源信号,但是在以往的算法中,第1步估计混叠矩阵时,通常假设源信号数目是已知的,从而进行K-均值聚类,而事实上源信号数目根本无法知道,因此对源信号数目的估计对两步法有很重要的影响。因此本文提出了一种新的两步法算法,其中第1步利用稀疏源信号反映在观测信号中的特征来准确地估计出稀疏源信号的数目,且能得到混叠矩阵,从而恢复源信号。最后的仿真结果,以及与通常的K-均值聚类算法对比的仿真结果说明了此算法的可行性和优异的性能。     

基于拉普拉斯势函数的欠定盲分离中源数的估计

本文提出了一种新的欠定盲源分离中源信号个数的估计算法,利用稀疏混合信号的特征,引入拉普拉斯势函数,并采用聚类算法来估计其局部最大值,由此得到源信号的个数估计.所提出的算法具有较好的抗噪声性能,对信号的稀疏度要求低.仿真实验结果说明了该算法的有效性.     

一种基于广义协方差矩阵的欠定盲辨识方法

针对欠定盲源分离模型中的混合矩阵估计问题,研究了一种基于广义协方差矩阵的欠定盲辨识方法。该方法利用观测信号采样数据的广义协方差矩阵性质,将构建的一系列模型函数堆叠为一个张量模型,进而将欠定的混合矩阵辨识转换为张量分解模型中秩一分量的估计。理论分析和仿真结果表明,基于广义协方差矩阵的欠定盲辨识方法比经典的基于二阶协方差矩阵和基于四阶累积量的欠定盲辨识方法具有优越的性能。     

稀疏分量分析在欠定语音信号盲分离中的应用

研究了基于两步法的欠定语音信号盲分离。针对混合信号散点图在原点中心混叠程度过高的缺点,提出了弭灭圆K均值聚类算法,提高了混叠矩阵的估计精度。结合时频分析算法实现了欠定瞬时线性混叠语音信号的盲分离,取得了较好的分离效果。     

基于孤立点检测的欠定混合矩阵盲辨识

本文将孤立点检测的思想引入到欠定混合矩阵的盲辨识问题,提出了一种基于孤立点检测的混合矩阵盲辨识方法.首先计算混合信号的空间时频分布并检测出单源时频点,然后检测出单源时频点中的孤立点并将其从中去除,再通过聚类的方法估计混合矩阵.该方法降低了对信号稀疏性的要求,通过去除数据中的孤立点,提高了矩阵的估计精度,同时也有助于对源信号数目的估计.仿真实验表明,与已有算法相比,本文方法进一步提高了混合矩阵的估计精度,并且有更强的鲁棒性.     

A method of underdetermined blind source separation in time-domain

In this article, we consider the problem of sources separation from their underdetermined instantaneous mixtures via time-domain. First, we introduced an algorithm based on differential kurtosis to separate non-stationary sources one by one under underdetermined case. Then, the rest sources are recovered using an algorithm based on second-order statistics. The simulation results show that the proposed method can separate sources of super- and sub-Gaussian distributions, and it has superior performance compared to other conventional methods.     

Application of underdetermined blind source separation in ultra-wideband communication signals

Aiming to the estimation of source numbers, mixing matrix and separation of mixing signals under underdetermined case, the article puts forward a method of underdetermined blind source separation （UBSS） with an application in ultra-wideband （UWB） communication signals. The method is based on the sparse characteristic of UWB communication signals in the time domain. Firstly, finding the single source area by calculating the ratio of observed sampling points. Then an algorithm called hough-windowed method was introduced to estimate the number of sources and mixing matrix. Finally the separation of mixing signals using a method based on amended subspace projection. The simulation results indicate that the proposed method can separate UWB communication signals successfully, estimate the mixing matrix with higher accuracy and separate the mixing signals with higher gain compared with other conventional algorithms. At the same time, the method reflects the higher stability and the better noise immunity.     

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。     

On regularity and identifiability of blind source separation under constant-modulus constraints

We investigate the information regularity and identifiability of the blind source separation (BSS) problem with constant modulus (CM) constraints on the sources. We establish for this problem the connection between the information regularity [existence of a finite Crame/spl acute/r-Rao bound (CRB)] and local identifiability. Sufficient and necessary conditions for local identifiability are derived. We also study the conditions under which unique (global) identifiability is guaranteed within the inherently unresolvable ambiguities on phase rotation and source permutation. Both sufficient and necessary conditions are obtained.     

基于欠定盲分离的多目标微多普勒特征提取

连续波雷达多目标回波中多种微多普勒特征分离问题采用独立成分分析方法实现,该方法在使用中存在较大局限性,要求待分离的微多普勒特征之间必须是统计独立的,且仅局限于恰定和超定的方程组求解问题。然而,在多目标雷达观测场景下,雷达接收的混叠回波的个数通常少于目标的个数,各目标的微多普勒特征可能存在相关性。为此,提出了一种基于欠定盲分离的多目标回波微多普勒特征分离方法。该方法可以从少数原始混叠回波中分离出多个目标的微多普勒特征,对待分离的微多普勒特征限制性弱。通过数值仿真,证实了该方法的可行性。     

A time-frequency blind separation method for underdetermined speech mixtures

The proposed Blind Source Separation method （BSS）, based on sparse representations, fuses time-frequency analysis and the clustering approach to separate underdetermined speech mixtures in the anechoic case regardless of the number of sources. The method remedies the insufficiency of the Degenerate Unmixing Estimation Technique （DUET） which assumes the number of sources a priori. In the proposed algorithm, the Short-Time Fourier Transform （STFT） is used to obtain the sparse representations, a clustering method called Unsupervised Robust C-Prototypes （URCP） which can accurately identify multiple clusters regardless of the number of them is adopted to replace the histogram-based technique in DUET, and the binary time-frequency masks are constructed to separate the mixtures. Experimental results indicate that the proposed method results in a substantial increase in the average Signal-to-Interference Ratio （SIR）, and maintains good speech quality in the separation results.     

Superefficiency in blind source separation

Blind source separation is the problem of extracting independent signals from their mixtures without knowing the mixing coefficients nor the probability distributions of source signals and may be applied to EEG and MEG imaging of the brain. It is already known that certain algorithms work well for the extraction of independent components. The present paper is concerned with superefficiency of these based on the statistical and dynamical analysis. In a statistical estimation using t examples, the covariance of any two extracted independent signals converges to 0 of the order of 1/t. On-line dynamics shows that the covariance is of the order of η when the learning rate η is fixed to a small constant. In contrast with the above general properties, a surprising superefficiency holds in blind source separation under certain conditions where superefficiency implies that covariance decreases in the order of 1/t² or of η² . The paper uses the natural gradient learning algorithm and method of estimating functions to obtain superefficient procedures for both batch estimation and on-line learning. A standardized estimating function is introduced to this end. Superefficiency does not imply that the error variances of the extracted signals decrease in the order of 1/t² or η² but implies that their covariances (and independencies) do     

Low-complexity single-channel blind source separation

For the time-frequency overlapped signals, a low-complexity single-channel blind source separation (SBSS) algorithm is proposed in this paper. The algorithm does not only introduce the Gibbs sampling theory to separate the mixed signals, but also adopts the orthogonal triangle decomposition-M (QRD-M) to reduce the computational complexity. According to analysis and simulation results, we demonstrate that the separation performance of the proposed algorithm is similar to that of the per-survivor processing (PSP) algorithm, while its computational complexity is sharply reduced.     

Improved statistical sparse decomposition principle method for underdetermined blind source signal recovery

Aiming at the statistical sparse decomposition principle (SSDP) method for underdetermined blind source signal recovery with problem of requiring the number of active signals equal to that of the observed signals, which leading to the application bound of SSDP is very finite, an improved SSDP (ISSDP) method is proposed. Based on the principle of recovering the source signals by minimizing the correlation coefficients within a fixed time interval, the selection method of mixing matrix's column vectors used for signal recovery is modified, which enables the choose of mixing matrix's column vectors according to the number of active source signals self-adaptively. By simulation experiments, the proposed method is validated. The proposed method is applicable to the case where the number of active signals is equal to or less than that of observed signals, which is a new way for underdetermined blind source signal recovery.     

General approach to blind source separation

This paper identifies and studies two major issues in the blind source separation problem: separability and separation principles. We show that separability is an intrinsic property of the measured signals and can be described by the concept of m-row decomposability introduced in this paper; we also show that separation principles can be developed by using the structure characterization theory of random variables. In particular, we show that these principles can be derived concisely and intuitively by applying the Darmois-Skitovich theorem, which is well known in statistical inference theory and psychology. Some new insights are gained for designing blind source separation filters