卷积混叠信号盲分离

本文讨论了卷积混叠信号盲分离问题，证明了一系列基于高阶谱的判据，指出了盲辨识和盲分离的充分条件，利用判据，发展了若干算法，模拟实验证实了判据的正确性和算法的有效性。     

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。     

基于互信息的卷积混合语音时域盲分离算法

本文以互信息最小化作为分离准则,提出了一种适用于非平稳语音卷积混合信号的时域盲分离算法。其目标函数同时考虑了语音信号的短时平稳性和长时非平稳性,在短时间段中计算平均互信息,在长时间段中引入权值因子,对短时间段中计算得到的平均互信息进行加权;其分离矩阵的更新采用快速收敛的自然梯度算法;其"去白化"的后处理步骤提高了分离语音的自然度。仿真实验和分析表明了算法的有效性。     

基于非连续多帧平滑的卷积混合盲分离频域算法

本文研究了卷积混合盲分离频域算法问题,基于短时傅立叶变换中各帧"部分卷积"的性质,提出了一种非连续多帧平滑的方法.该方法有助于降低源信号"短时谱"的瞬时混合和卷积混合信号的短时谱之间误差,从而提升分离性能.仿真实验证实了提出算法的有效性.     

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.     

On-line Convolutive Blind Source Separation of Non-Stationary Signals

We have shown previously that non-stationary signals recorded in a static multi-path environment can often be recovered by simultaneously decorrelating varying second order statistics. As typical sources are often moving, however, the multi-path channel is not static. We present here an on-line gradient algorithm with adaptive step size in the frequency domain based on second derivatives, which we refer to as multiple adaptive decorrelation (MAD). We compared the separation performance of the proposed algorithm to its off-line counterpart and to another decorrelation based on-line algorithm.     

基于信道缩短的超宽带信道盲估计方法

在基于一阶统计量解圆周卷积的超宽带信道盲估计方法中,存在着要求信道长度必须小于符号周期长度的局限性.使用信道缩短技术,对实际信道的长度进行缩短,估计出缩短信道后再恢复成实际的超宽带信道,有效地解决了这个问题.仿真实验表明,在信道长度大于符号周期长度时,仍然能很好地估计出信道.     

Using the Penalized Mutual Information Criterion in the Multivariate Edgeworth-Expanded Gaussian Mixture Density for Blind Separation of Convolutive Post-Nonlinear Mixtures

This paper proposes the blind separation of convolutive post-nonlinear (CPNL) mixtures based on the minimization of the penalized mutual information criterion. The proposed algorithm is based on the estimation score function difference (SFD) and the Newton optimization. Compared with the blind source separation of a linear mixture, the separation performance of a nonlinear mixture is strongly related to the accuracy of the score function estimation. Under this framework, the multivariate Edgeworth-expanded Gaussian mixture density is adopted to estimate the SFD, which preserves the higher-order statistical structure of the data as compared to the nonparametric density estimation. Also, the Newton optimization converges faster than the steepest descent gradient. In order to calculate the Hessian matrix, the Taylor expansion of the penalized mutual information criterion is extended to second order. The minimization of the penalized mutual information criterion ensures a priori normalization of the estimated sources, thus avoiding scale indeterminacy. The proposed algorithm has a better performance, and at the same time it speeds up the convergence. Simulations with computer-generated data and synthetic real-world data show the effectiveness of the proposed algorithm.     

瞬时混叠信号盲分离

本文利用三阶和四阶积累量研究了瞬时混叠信号分离的问题－根据源信号通过瞬时叠加后的观测值忧愁出源信号。     

Quadratic Higher Order Criteria for Iterative Blind Separation of a MIMO Convolutive Mixture of Sources

This paper deals with the problem of source separation in the case when the observations result from a multiple-input multiple-output convolutive mixing system. In a blind framework, higher order contrast functions have been proved to be efficient for extracting sources. Inspired by a semiblind approach, we propose new contrast functions for blind signal separation that make use of reference signals. The main advantage of this approach consists in the quadratic form of these criteria: the extraction of one source hence reduces to a simple optimization task for which fast and efficient algorithms are available. The separation of the other sources from the mixture is then carried out by an iterative deflation method. Furthermore, these contrasts are shown to be valid for both independent identically distributed (i.i.d.) and non-i.i.d. source signals. The performance offered by these criteria is investigated through simulations: they appear as very appealing tools compared with some classical contrast functions     

源信号数目大于观察信号数目情况下的盲源分离

独立分量分析(ICA)作为一种有效的盲源分离技术(BSS）是信号处理领域的热点。传统的独立分量分析都要求观察信号数目大于或者等于源信号数目，然而对于脑电图(EEG)等的一些信号处理中存在的源信号数目大于观察信号数目的情况，传统的独立分量分析算法不能有效分离。该文针对源信号数目大于观察信号数目的情况，在传统的独立分量分析技术的基础上，给出了一个新的学习算法，并将新算法与传统的独立分量算法进行了比较。实验仿真结果证明该算法在给定2个混合信号的情况下能够较好地分离3个未知语音信号源，成功实现了源信号数目大于观察信号数目情况下的盲源分离。     

Joint Blind Adaptive Carrier Frequency Offset Estimation and Channel Shortening

Multicarrier modulation has the advantage of robustness to multipath, so long as the delay spread of the multipath is less than the guard interval between successive transmitted blocks. However, it has the disadvantage of sensitivity to a carrier frequency offset (CFO), which disrupts the orthogonality of the subcarriers. If a CFO is present, it must be estimated and then corrected. In addition, if the channel delay spread exceeds the guard interval, this must be accounted for as well, usually by a channel shortening equalizer. Hitherto, CFO estimation and channel shortening have been addressed individually. However, this leads to a circular set of assumptions, since standard CFO estimators perform poorly when the channel is longer than the guard interval, and standard channel shorteners assume that CFO estimation has already been perfectly performed. In this paper, we investigate a blind, adaptive method for jointly performing CFO estimation and channel shortening.     

基于盲信号分离的信道均衡算法

本文提出了一个新的基于盲信号分离的信道均衡结构和算法。通过对接收信号的过采样,由源序列构成的、长度为N的矢量可以被看成互相独立的N个信号源,与此相应的接收矢量则是该N个独立信号通过线性系统后的输出。为了恢复被传送的序列,我们采用基于神经网络学习的盲信号分离算法,实现信道的盲均衡。模拟结果显示,无论是实信道还是复数信道,该方法都具有较好的均衡效果。     

Simultaneous Blind Separation of Instantaneous Mixtures With Arbitrary Rank

This paper presents a gradient-based method for simultaneous blind separation of arbitrarily linearly mixed source signals. We consider the regular case (i.e., the mixing matrix has full column rank) as well as the ill-conditioned case (i.e., the mixing matrix does not have full column rank). We provide one necessary and sufficient condition for the identifiability of simultaneous blind separation. According to our identifiability condition and the existing general identifiability condition, all source signals are separated into two categories: separable single sources and inseparable mixtures of several single sources. A sufficient condition is also derived for the existence of optimal partition of the mixing matrix which leads to a unique maximum set of separations. One sufficient condition is proved to show that each maximum partition of the mixing matrix corresponds to a unique class of separated signals and as a result we can determine the number of maximum partitions from the classes of outputs under different separation matrices. For sub-Gaussian or super-Gaussian source signals, a cost function based on fourth-order cumulants is introduced to simultaneously separate all separable single sources and all inseparable mixtures. By minimizing the cost function, a gradient-based method is developed. Finally, simulation results show the effectiveness of the present method.     

Widely Linear Versus Linear Blind Multiuser Detection With Subspace-Based Channel Estimation: Finite Sample-Size Effects

In a recent paper [A. S. Cacciapuoti et al., “Finite-Sample Performance Analysis of Widely Linear Multiuser Receivers in DS-CDMA Systems, IEEE Transactions on Signal Processing, vol. 56, no. 4, pp. 1572–1588, Apr. 2008], we presented the finite-sample theoretical performance comparison between linear (L) and widely linear (WL) minimum output-energy (MOE) receivers for direct-sequence code-division multiple-access (DS-CDMA) systems, worked out under the assumption that the channel impulse response of the desired user is exactly known. The main scope of this paper is to extend such an analysis, taking into account not only autocorrelation matrix (ACM) estimation effects, but also the accuracy of subspace-based blind channel estimation (CE). We aim to answer the two following questions: Which of the two estimation processes (ACM or CE) is the main source of degradation when implementing the receivers on the basis of a finite sample-size? Compared with the L-MOE one, is the finite-sample WL-MOE receiver with blind CE capable of achieving the performance gains predicted by the theory? To this goal, simple and easily interpretable formulas are developed for the signal-to-interference-plus-noise ratio (SINR) at the output of the L- and WL-MOE receivers with blind CE, when they are implemented using either the sample ACM or its eigendecomposition. In addition, the derived formulas, which are validated by simulations, allow one to recognize and discuss interesting tradeoffs between the main parameters of the DS-CDMA system.      

带噪的战场声信号盲分离方法研究

提出了一种噪声环境中战场混合声信号盲分离方法.基于含噪的独立分量分析模型,对观测信号进行准白化,去除噪声引起的协方差偏移量;定义观测信号中随机变量的高斯矩为无偏估计的目标函数,最大化此目标函数得到了一种改进的FastICA算法,应用于带噪的战场混合声信号盲分离.仿真实验证明,改进算法能较好改善分离效果,具有很好的鲁棒性.     

Closed-Form Estimators for Blind Separation of Sources – Part I: Real Mixtures

The problem of multiuser interference cancellation in wireless cellularcommunication systems accepts a blind source separation (BSS) model. Thepresent contribution studies the closed-form solutions to BSS in thereal-mixture case. Connections among a number of seemingly disparate methodsare unveiled, new procedures are put forward, and their asymptotic(large-sample) performance is analyzed. Simulation experiments illustrate andvalidate the theoretical results. Altogether, a unifying generic framework forclosed-form BSS methods is developed.     

Closed-Form Estimators for Blind Separation of Sources – Part II: Complex Mixtures

The problem of multiuser detection in wireless communications systems adopts,in flat-fading channels, a blind source separation (BSS) formulation ofinstantaneous linear mixtures. This contribution addresses the closed-formsolutions to BSS in the complex-mixture scenario. The algebraic devices whichspan a unifying framework for the complex BSS closed-form estimators aredeveloped. With the aid of these tools, results originally encountered in thereal-mixture case are extended to the complex case, thus highlighting theremarkable parallelism existing between the real and complex problems in thecontext of their analytic solutions. Computer simulations illustrate thetheoretical results and compare the proposed methods to other BSS procedures.     

单通道盲信号分离研究进展与展望

盲信号分离是当前国际信号处理领域的研究热点,作为一种病态混叠情况,单通道盲信号分离是其刚刚兴起的一个重要研究方向,有着广泛的应用前景。首先阐述了单通道盲信号分离的数学模型、可分离性分析和分离效果评价指标,然后在深入研究单通道盲信号分离最新发展的基础上,对现有理论和方法进行了总结,并介绍了一些领域中的应用情况,最后指出其发展面临的挑战和进一步的研究方向。     

阵列天线多载波DS-CDMA系统基于子空间的信道估计

阵列天线多载波DS-CDMA系统在未来移动通信中具有重要的应用前景。本文研究了阵列天线多载波DS-CD MA系统基于子空间的信道盲估计方法。通过在阵列天线多载波DS-CDMA系统下构造统一的信号模型,实现了基于子空间的信道盲估计。仿真结果表明所提出的方法可有效地对阵列天线多载波DS-CDMA系统的信道参数进行估计,并可抵抗适度的远近效应。