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。     

基于改进K-means聚类和霍夫变换的稀疏源混合矩阵盲估计算法

 混合矩阵的估计是稀疏源盲分离的关键组成部分,其估计精度直接影响到源信号的估计精度.本文首先针对K-means聚类算法依赖初始值选取的问题,将微分进化算法思想引入到K-means聚类算法中,提出了一种改进的K-means聚类算法.利用该算法,对稀疏源混合信号数据进行聚类,保证了聚类结果的鲁棒性.然后利用霍夫变换,对每一类数据的聚类中心进行修正,从而估计出混合矩阵,提高了混合矩阵的估计精度.仿真实验表明,相比于经典的稀疏源混合矩阵盲估计算法,本文算法具有更强的鲁棒性和更高的估计精度.     

基于时频分析的混合矩阵估计方法

在盲源分离信号处理中,尤其在欠定条件下(观测信号数目大于源信号数目),精确的估计混合矩阵是具有挑战性的问题。现存部分方法利用信号的稀疏性进行求解,并假设在时域或者时频域中源信号不重叠,然而这类方法在假设条件不满足,即源信号部分重叠情况下随着信号稀疏性降低性能恶化明显。本文针对具有较弱稀疏性的源信号,提出了一种基于时频分析的欠定盲源分离的混合矩阵估计方法。首先,利用源信号时频变换后系数实部与虚部比值的差异性选择单源点;其次,运用经典的聚类方法估计解混合矩阵的各向量。仿真结果表明:提出的方法简易可行并具有较好的估计性能。      

基于欠定盲源分离的同步跳频信号网台分选

针对同步跳频(FH)网台分选问题,该文提出一种基于时频域单源点检测的欠定盲源分离(UBSS)分选算法.该算法首先对观测信号时频变换,利用自适应阈值去噪算法消除时频矩阵背景噪声,增加算法抗噪性能,然后根据信号绝对方位差算法进行单源点检测,有效保证单源点的充分稀疏性,并通过改进的模糊值聚类算法完成混合矩阵和2维波达方向估计,降低噪声和样本集分布差异对聚类结果的影响,提高估计精度.最后采用变步长的稀疏自适应子空间追踪(SASP)算法对源信号进行重构恢复.仿真实验表明,该算法在低信噪比(SNR)条件下,跳频信号波达方向估计和恢复精度较高,能够有效完成同步跳频信号的盲分离.     

基于欠定盲源分离的同步跳频信号网台分选

针对同步跳频(FH)网台分选问题,该文提出一种基于时频域单源点检测的欠定盲源分离(UBSS)分选算法.该算法首先对观测信号时频变换,利用自适应阈值去噪算法消除时频矩阵背景噪声,增加算法抗噪性能,然后根据信号绝对方位差算法进行单源点检测,有效保证单源点的充分稀疏性,并通过改进的模糊值聚类算法完成混合矩阵和2维波达方向估计...     

一种多维信源衰减延时混合的欠定盲源分离方法

为解决衰减延时混合信号的欠定盲源分离问题,该文研究了一种基于信源数估计的欠定盲源分离方法.首先,采用对时频域观测信号求能量来构造稀疏域;其次,在能量域中利用势函数估计信源数;再次,根据信源数将能量和峰值对应的频点筛选出来预测时频掩码从而获得估计信源的短时频谱;最后,填充线用来解决时域分离信号的边界效应问题.实验表明,所提方法可以有效分离衰减延时混合的模拟信号,并且在不同信噪比下优于稀疏聚类算法和子空间法;此外,在对实测悬臂梁锤击测试的过程中可以估计出模态阶数并且准确识别出结构的各阶模态固有频率.     

一种多维信源衰减延时混合的欠定盲源分离方法

为解决衰减延时混合信号的欠定盲源分离问题，该文研究了一种基于信源数估计的欠定盲源分离方法。首先，采用对时频域观测信号求能量来构造稀疏域；其次，在能量域中利用势函数估计信源数；再次，根据信源数将能量和峰值对应的频点筛选出来预测时频掩码从而获得估计信源的短时频谱；最后，填充线用来解决时域分离信号的边界效应问题。实验表明，所提方法可以有效分离衰减延时混合的模拟信号，并且在不同信噪比下优于稀疏聚类算法和子空间法；此外，在对实测悬臂梁锤击测试的过程中可以估计出模态阶数并且准确识别出结构的各阶模态固有频率。     

小波域欠定盲图像混合矩阵估计研究

欠定盲源分离已经成为当前盲信号处理的研究热点,欠定的盲图像分离技术在现实的科研和生产实践中有重要的研究意义。由于图像信号的本身特征,直接进行处理不能满足欠定盲源分离的条件,文章所做的工作就是将盲图像的混合图像进过一层小波变换,在小波域得到了充分稀疏的高频分量,然后利用超平面法矢量聚类算法在小波域进行混合矩阵的估计。通过仿真实验,对比传统算法,得到了较好的估计结果。     

基于超平面法矢量的欠定盲混合矩阵估计

源信号稀疏性差时,基于源信号稀疏特性的欠定盲混合矩阵估计算法,通常先聚类求得混合矢量张成的超平面,然后估计混合矩阵。但此方法涉及运算量较大的超平面聚类,算法效率低。针对这一缺陷,提出了一种新的混合矩阵估计算法。先由所提出的基于梯度法的法矢量更新方法求得超平面法矢量的估计,然后求出混合矩阵。该方法不需要进行超平面聚类,大大降低了运算量,提高了混合矩阵估计效率。仿真结果证明了该方法的正确性和有效性。     

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.     

Underdetermined BSS Based on <Emphasis Type="Italic">K</Emphasis>-means and AP Clustering

Underdetermined blind source separation (UBSS) is a hard problem to solve since its mixing system is not invertible. The well-known “two-step approach” has been widely used to solve the UBSS problem and the most pivotal step is to estimate the underdetermined mixing matrix. To improve the estimation performance, this paper proposes a new clustering method. Firstly, the observed signals in the time domain are transformed into sparse signals in the frequency domain; furthermore, the linearity clustering of sparse signals is translated into compact clustering by normalizing the observed data. And then, the underdetermined mixing matrix is estimated by clustering methods. The K-means algorithm is one of the classical methods to estimate the mixing matrix but it can only be applied to know the number of clusters in advance. This is not in accord with the actual situation of UBSS. In addition, the K-means is very sensitive to the initialization of clusters and it selects the initial cluster centers randomly. To overcome the fatal flaws, this paper employs affinity propagation (AP) clustering to get the exact number of exemplars and the initial clusters. Based on those results, the K-means with AP clustering as initialization is used to precisely estimate the underdetermined mixing matrix. Finally, the source signals are separated by linear programming. The experimental results show that the proposed method can effectively estimate the mixing matrix and is more suitable for the actual situation of UBSS.     

一种强鲁棒性的盲分离混合矩阵估计方法

针对传统盲分离混合矩阵估计鲁棒性差、易受初始值影响、精度不高等问题,该文将人工蜂群算法(ABC)用到盲分离中,结合稀疏信号混合矩阵估计的特点,提出一种基于不同搜索策略和编码方式的两阶段蜂群算法的混合矩阵估计方法,通过新的蜜蜂搜索行为和子蜂群之间的协同作业,明显加快了算法的收敛速度,提高了混合矩阵的估计精度。仿真实验表明,该方法在源个数较多、弱稀疏、低信噪比的情况下仍然可以很好地估计混合矩阵。相比已有方法,该方法不仅具有很强的鲁棒性和很高的估计精度,而且不需要太大的计算量。     

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。     

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。     

基于阵列结构的盲源分离算法

利用源信号的统计独立性提出了一种由混合信号和分离信号估计混合矩阵的算法,避免了一次矩阵求逆运算。同时,利用天线阵列的结构信息提出了一种直接重构混合矩阵的算法,该算法不需要估计源信号的载频和入射角。仿真结果表明,所提算法与基于来波方向估计的盲分离算法性能相当,在信号入射角间隔较大时,所提算法性能优于普通的盲分离算法,并且大大减少了运算量。     

Postprocessing and sparse blind source separation of positive and partially overlapped data

We study sparse blind source separation (BSS) for a class of positive and partially overlapped signals. The signals are only allowed to have nonoverlapping at certain locations, while they could overlap with each other elsewhere. For nonnegative data, a novel approach has been proposed by Naanaa and Nuzillard (NN) assuming that nonoverlapping exists for each source signal at some location of acquisition variable. However, the NN method introduces errors (spurious peaks) in the output when their nonoverlapping condition is not satisfied. To resolve this problem and improve robustness of separation, postprocessing techniques are developed in two aspects. One is to detect coherent and uncertain components from NN outputs by using multiple mixture data, then removing the uncertain portion to enhance signals. The other is to find better estimation of mixing matrix by leveraging reliable source peak structures in NN output. Numerical results on examples including NMR spectra of a ¹³C-1-acetylated carbohydrate with overlapping proton spin multiplets show satisfactory performance of the postprocessed sparse BSS and offer promise to resolve complex spectra without using multidimensional NMR methods.     

Compressed Blind Signal Reconstruction Model and Algorithm

The model of inherent connection between underdetermined blind signal separation and compressed sensing (CS) is analyzed first; then, the mathematical model of underdetermined blind signal reconstruction is built using CS. More specifically, the mixing matrix is estimated by exploiting the wavelet packet transform and k-means clustering methods up to permutation and scaling indeterminacy, and then, the measurement matrix and the measurement equation are obtained. To reconstruct the underdetermined sparse source signals, the proposed semi-blind compressed reconstruction algorithm is derived based on the blind signal reconstruction model and compressive sampling matching pursuit (CoSaMP) method. Our simulation results demonstrate that the proposed scheme is effective, irrespective of artificial data or real data. Moreover, the proposed scheme can be adjusted for different applications by modifying the mixing matrix estimation method and CoSaMP method with respect to the correspondence conditions.     

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.