结合ICA和复数神经网络的二麦阵列盲源分离技术

为了降低语音信号盲源分离算法的延时,提高其准确性和稳定性,本文结合传统盲源分离技术和深度神经网络的优势,提出了一种基于ICA独立分量分析和复数神经网络的二麦阵列盲源分离技术。本文将复数递归神经网络和独立分量分析方法有机融合,提出一种基于时频域的双通道复数神经网络,同时解决了独立分量分析中的排列问题。所提方法利输入混合信号利用复数域神经网络计算初始化分离矩阵,神经网络输出采用复数域形式,利用复数学习标签估计复数矩阵,然后采用独立分量分析方法获得目标分离矩阵。实验数据表明,所提方法相较于其它独立分量分析方法提高了盲源分离的实时性和准确性。      

基于复数FastICA算法的短波同频信号盲源分离

盲源分离是一种经典信号处理方法,可广泛应用于通信信号、阵列处理的混合信号有效分离。将一种基于独立分量分析的复数信号盲源分离算法应用于实际的短波信号分离中,能有效地实现对各种信号组合的分离。     

基于频域模型的宽带信号子空间谱估计方法

宽带阵列信号是频率的函数,因此其阵列流型及协方差矩阵都随频率变化.本文基于宽带信号的频域模型,通过分析宽带阵列信号协方差矩阵的特征分解结构,证明了宽带阵列信号噪声子空间不随频率变化的特性,并根据这一特性,提出了基于频域模型的宽带子空间谱估计(SSEFD)方法.应用K.Buckley的BASS-ALE方法解决了该方法用于均匀线阵时存在的频率-方位模糊问题.计算机仿真结果验证了SSEFD方法的有效性,与H.Wang的CSS方法的统计性比较表明,新方法具有更高的估计精度.     

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。     

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.     

非均匀噪声环境下宽带近场源定位的两种改进方法

研究了宽带近场信号源基于最大似然方法和相关信号子空间方法在非均匀噪声下的被动定位算法，并进行了比较。这两种算法均可在传感器任意分布的情况下有效地进行信号源定位。最大似然法采用了迭代的方法来估计噪声的协方差矩。而信号子空间法给出了聚焦阵构造的新方法。仿真试验证明了方法的有效性和稳健性。     

Wideband array processing using a two-sided correlationtransformation

A new method for broadband array processing is proposed. The method is based on unitary transformation of the signal subspaces. We apply a two-sided transformation on the correlation matrices of the array. It is shown that the two-sided correlation transformation (TCT) has a smaller subspace fitting error than the coherent signal-subspace method (CSM). It is also shown that unlike CSM, the TCT algorithm can generate unbiased estimates of the directions-of-arrival, regardless of the bandwidth of the signals. The capability of the TCT and CSM methods for resolving two closely spaced sources is compared. The resolution threshold for the new technique is much smaller than that for CSM     

基于盲源分离理论的麦克风阵列信号有音/无音检测方法

该文提出一种在方向性噪声场中多路麦克风信号同时进行有音/无音检测(VAD)的方法。在方向性噪声场中,由于各个麦克风接收信号中的噪声彼此之间相关,因而,可以利用盲源分离理论将方向噪声与语音源信号分离,从而获得相对比较纯净的语音源信号。对分离出的语音源信号进行有音/无音检测,获得VAD结果,同时估计出各个麦克风信号相对于该信号的时延值。以相对纯净语音源信号的VAD检测结果为参考,将其分别平移相应的时延值,即可同时获得多路麦克风信号的VAD结果。计算机模拟结果表明,在方向性噪声场的多种情况下,该方法对具有加性噪声的多路麦克风信号均具有较好的有音/无音检测能力。     

Blind signal separation: statistical principles

Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis that aim to recover unobserved signals or “sources” from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mutual independence between the signals. The weakness of the assumptions makes it a powerful approach, but it requires us to venture beyond familiar second order statistics, The objectives of this paper are to review some of the approaches that have been developed to address this problem, to illustrate how they stem from basic principles, and to show how they relate to each other     

基于盲源分离的单通道窄带干扰抑制算法

针对窄带干扰下通信系统性能恶化的问题,提出一种基于过采样与盲源分离技术的单通道窄带干扰抑制算法。该算法利用通信信号与窄带干扰的基带结构特征,通过过采样以及串并变换构造出源信号为信息序列的盲源分离模型,并利用快速独立成分分析法实现信息码元的恢复。仿真结果表明,该算法能有效对抗窄带干扰,抗干扰能力强。     

Spatiotemporal blind source separation approach to atrial activity estimation in atrial tachyarrhythmias

The analysis and characterization of atrial tachyarrhythmias requires, in a previous step, the extraction of the atrial activity (AA) free from ventricular activity and other artefacts. This contribution adopts the blind source separation (BSS) approach to AA estimation from multilead electrocardiograms (ECGs). Previously proposed BSS methods for AA extraction-e.g., independent component analysis (ICA)-exploit only the spatial diversity introduced by the multiple spatially-separated electrodes. However, AA typically shows certain degree of temporal correlation, with a narrowband spectrum featuring a main frequency peak around 3.5-9 Hz. Taking advantage of this observation, we put forward a novel two-step BSS-based technique which exploits both spatial and temporal information contained in the recorded ECG signals. The spatiotemporal BSS algorithm is validated on simulated and real ECGs from a significant number of atrial fibrillation (AF) and atrial flutter (AFL) episodes, and proves consistently superior to a spatial-only ICA method. In simulated ECGs, a new methodology for the synthetic generation of realistic AF episodes is proposed, which includes a judicious comparison between the known AA content and the estimated AA sources. Using this methodology, the ICA technique obtains correlation indexes of 0.751, whereas the proposed approach obtains a correlation of 0.830 and an error in the estimated signal reduced by a factor of 40%. In real ECG recordings, we propose to measure performance by the spectral concentration (SC) around the main frequency peak. The spatiotemporal algorithm outperforms the ICA method, obtaining a SC of 58.8% and 44.7%, respectively.     

高分辨率宽带相干信号波达方向的高效估计

本文给出了一种用于多源宽带相干信号波达方向(DOA)估计的快速算法。它是基于信号子空间技术,通过截取中心窄带频段数据并进行空间平滑来获得对方向的高效估计。文中最后通过计算机仿真,与CSM方法进行了比较,验证了本算法的快速、高分辨率等优良性能。     

基于虚拟旋转不变技术的宽带信号二维到达角估计

在相关信号子空间方法的基础上,本文提出了一种阵列宽带信号二维角度的估计方法。该方法首先利用虚拟互相关计算方法得到阵列输出的协方差矩阵,并构造出两个子阵(实际子阵和虚拟子阵);然后采用投影算子来形成聚焦矩阵,最后对聚焦后的协方差矩阵采用ESPRIT方法估计出宽带信号的二维到达角。这种方法能抑制非高斯噪声对算法的影响,并能扩展阵列孔径,且不需要进行角度预估计;估计出的二维角度能自动配对,提高了算法的实现速度。计算机仿真试验证实了该算法的有效性。     

盲信号分离在数字无线通信中的一种应用

本文提出了基于神经网络的盲信号分离在数字无线通信中的一种应用。利用本文中的天线阵,接收到的信号可以看作是由Ｎ个独立的信号源所激励的线性混合系统的输出,应用基于神经网络的鲁棒性很好的盲分离算法＾〖７〗,实现多用户信号的分离。我们还就其在数字无线通讯中的应用进行了计算机模拟,模拟结果显示分离效果是令人满意的。     

非线性盲源分离的原理及算法综述

本文主要阐述了非线性盲源分离(BSS)/独立成分分析(ICA)模型的基本数学原理、分离算法、算法性能及其应用。首先对线性和非线性BSS/ICA的数学模型作了介绍,重点介绍了非线性BSS/ICA解的不确定性,然后在此基础上对近十年来出现的各种非线性BSS/ICA算法进行简单综述,着重分析了一类可解且应用比较广泛的非线性BSS/ICA模型-后非线性BSS/ICA模型及其分离算法。最后对非线性BSS/ICA存在的问题和发展趋势进行了总结。     

Non-linear independent component analysis using series reversion and Weierstrass network

The problem of blind separation of independent sources in non-linear mixtures is considered and the focus of this work is on a new type of non-linear mixture in which a linear mixing matrix is sandwiched between two mutually reverse non-linearities. The demixing system culminates to a novel Weierstrass network that is shown to successfully restore the original source signals under the non-linear mixing conditions. The corresponding parameter learning algorithm for the proposed network is presented through formal mathematical derivation. The authors show for the first time a new result based on the theory of forward series and series reversion that is integrated into a neural network to implement the proposed demixer. Simulations, including both synthetic and recorded signals, have been carried out to verify the efficacy of the proposed method. It is shown that the Weierstrass network outperforms other tested independent component analysis (ICA) methods (linear ICA, radial-basis function and multilayer perceptron network) in terms of speed and accuracy.     

MIMO-AR System Identification and Blind Source Separation for GMM-Distributed Sources

The problem of blind source separation (BSS) and system identification for multiple-input multiple-output (MIMO) auto-regressive (AR) mixtures is addressed in this paper. Two new time-domain algorithms for system identification and BSS are proposed based on the Gaussian mixture model (GMM) for sources distribution. Both algorithms are based on the generalized expectation-maximization (GEM) method for joint estimation of the MIMO-AR model parameters and the GMM parameters of the sources. The first algorithm is derived under the assumption of unstructured input signal statistics, while the second algorithm incorporates the prior knowledge about the structure of the input signal statistics due to the statistically independent source assumption. These methods are tested via simulations using synthetic and audio signals. The system identification performances are tested by comparison between the state transition matrix estimation using the proposed algorithms and the well-known multidimensional Yule-Walker solution followed by an instantaneous BSS method. The results show that the proposed algorithms outperform the Yule-Walker based approach. The BSS performances were compared to other convolutive BSS methods. The results show that the proposed algorithms achieve higher signal-to-interference ratio (SIR) compared to the other tested methods.     

Optimal Pairwise Fourth-Order Independent Component Analysis

Blind source separation (BSS) aims at the reconstruction of unknown mutually independent signals, so-called sources, from their mixtures observed at the output of a sensor array. The BSS of instantaneous linear mixtures, which finds application in numerous fields, can be solved through the statistical tool of independent component analysis (ICA). This paper concentrates on the analytic solutions for the fundamental two-signal ICA scenario. A novel estimation class, so-called general weighted fourth-order estimator (GWFOE), is put forward, which is based on the fourth-order statistics of the whitened sensor output. By means of a weight parameter, the GWFOE is able to unify a variety of apparently disparate estimation expressions previously scattered throughout the literature, including the well-known JADE method in the two-signal case. A theoretical asymptotic performance analysis is carried out, resulting in the GWFOE large-sample mean square error and the source-dependent weight value of the most efficient estimator in the class. To extend the pairwise estimators to the general scenario of more than two sources, an improved Jacobi-like optimization technique is proposed. The approach consists of calculating the necessary sensor-output fourth-order statistics at the initialization stage of the algorithm, which can lead to significant computational savings when large sample blocks are processed. Based on this idea, adaptive algorithms are also devised, showing very satisfactory convergence characteristics. Experiments illustrate the good performance of these optimal pairwise ICA strategies, in both off- and on-line processing modes.     

基于ICA的无线电通信干扰抑制

抑制无线电通信信号中的干扰是提高通信可靠性的有效措施.传统的干扰抑制技术存在算法、设备复杂,对信号有损伤和实时性差等问题.针对存在的问题,在分析研究盲源分离(BSS)的理论基础上建立了基于最大信噪比算法的ICA通信干扰抑制模型;基于该模型仿真实现了无线电通信信号的分离提取和多路干扰信号的抑制.仿真结果表明,基于ICA的...     

空间电子探测信号盲分离研究

给出了盲信号分离中的瞬时混合,时延混合和卷积混合三种混合模型,介绍了两种具体的盲分离算法,等变自适应盲分离算法和非高斯性最大化的快速定点算法.其中对于窄带源信号,对时延混合模型进行了扩展,提出了用复数域瞬时盲信号分离算法分离时延混合信号的新思路.最后给出了相应的仿真和实验结论,实验结果表明用基于复数的盲分离算法确实能够有效地分离阵列接收的时延混合信号.