乘性和加性噪声中二维谐波频率估计

噪声中的谐波恢复问题是信号处理领域的一个典型问题,在众多领域中有着广泛的应用。本文主要研究零均值乘性和加性噪声并存下的二维谐波信号频率估计问题,提出了一种基于数据矩阵的奇异值分解和子空间的旋转不变性的零均值乘性和加性噪声中的谐波频率的估计方法。乘性噪声为零均值情形下传统的估计方法往往难以直接应用或估计失效。本文利用谐波模型信号特征,通过对观测信号进行平方运算构造了一个数据矩阵。通过对数据矩阵的特征值进行理论分析,结合子空间旋转不变性,得到了零均值乘性和加性噪声中的谐波频率和数据矩阵之间的一种内在关系。这个性质可以用于零均值乘性和加性噪声并存下的二维谐波信号频率估计,并且所得的二维频率能自动配对。仿真实验验证了本文所提算法的有效性。      

基于子空间旋转不变性的加性有色噪声中谐波频率的估计

噪声中的谐波恢复问题是信号处理领域的一个典型问题,在众多领域中有着广泛的应用。本文主要研究加性有色噪声中谐波频率的估计问题,提出了一种基于子空间旋转不变性的谐波频率的高分辨率估计方法。利用观测信号的自协方差函数构造了一个协方差矩阵,通过对协方差矩阵的特征值进行理论分析,结合子空间旋转不变性,得到了加性有色噪声中谐波的频率和协方差矩阵之间的一种内在联系。利用这个性质可以估计加性有色噪声中谐波的频率。本文方法对于有色噪声的模型无任何假设,而且对于噪声的分布也没有限制,对于高斯和非高斯有色噪声都适用。仿真实验验证了本文所提算法的有效性。      

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。     

A Utility-Based Joint Resource Allocation Approach for Multi-Service in CoMP Networks

One of the premier mechanisms used in extracting unobserved signals from observed mixtures in signal processing is employing a blind source separation (BSS) algorithm or technique. A prominent role in the sphere of multicarrier communication is played by orthogonal frequency division multiplexing (OFDM) techniques. A set of remedial solutions taken to mitigate deteriorative effects caused within the air interface of an OFDM transmission with aid of BSS schemes is presented. Two energy functions are used in deriving the filter coefficients. They are optimized and performance is justified. These functions with the iterative fixed point rule for receive signal are used in determining the filter coefficients. Time correlation properties of the channel are taken advantage for BSS. It is tried colored noise and interference components to be removed from the signal mixture at the receiver. The method is tested in a slow fading channel with a receiver containing equal gain combining to treat the channel state information values. The importance is that, these solutions can be noted as quite low computational complexity mechanisms.     

Kalman滤波下的多信号单通道盲源分离

窄带物联网环境中,接收机收到的信号通常为多路混合信号,对单通道接收来说,利用常规盲源分离方法很难实现混合信号的分离和源信号提取。针对这一问题,本文提出了一种利用Kalman滤波算法进行信号估计,解决单通道盲源分离的方法。该方法利用信号间的时序结构,通过Kalman滤波算法对多信号混合中的源信号不断估计并迭代更新,最终得到分离信号。仿真实验结果表明,该方法能有效估计并分离出源信号。      

基于雷达系统抗干扰的卷积盲分离算法研究

如何提高雷达系统的抗干扰能力一直是雷达信号处理中比较关注的问题。卷积混合盲分离技术是当前盲分离研究的热点和难点。文中用卷积混合盲分离算法来分离雷达系统接收的信号,把卷积混合的干扰信号和有用信号分离开来,以实现雷达系统抗干扰的目的。该方法使用基于Householder变换的高阶累积量的联合对角化的频域方法来分离卷积混合的雷达接收信号。计算机仿真表明,该抗干扰方法可把卷积混合的雷达信号和干扰信号分离出来,且达到较好的分离效果。     

New natural gradient algorithm for cyclostationary sources

A new natural gradient type algorithm (NGA) for the separation of cyclostationary sources is introduced. Based on the interpretation of blind source separation (BSS) as a two-stage process, including prewhitening and rotation, the cyclostationary NGA (CSNGA) algorithm is constructed such that it also ensures that the recovered sources are decorrelated in the cyclostationary sense. The method is generalised to the case of complex valued source signals, and modified so that adequate algorithm performance is attained even when only one source cycle frequency is known. The properties of the new algorithm are investigated when additive white Gaussian noise is present, and it is found that, in general, the CSNGA approach improves the convergence properties of the natural gradient algorithm. Computer simulations support the validity of the approach.     

基于二阶矩的雷达信号盲分离

针对雷达接收机在现代战场复杂电磁环境下接收到的混叠信号,提出了一种基于二阶矩的信号盲源分离方法。在混合信号球化过程中,对于具有加性白噪声的模型,构造了一组新的协方差矩阵,在信噪比不是很高的情况下,使其不会影响分离结果。在协方差矩阵对角化过程中,采用自然梯度的方法,避免分离矩阵更新过程中的求逆问题,提高了算法的实时性。仿真实验证明,在信噪比为-10 dB的条件下,对比FastICA算法,所提算法分离精度高,收敛速度快,为进一步的信号识别提供可靠依据。     

Multichannel Convolution Blind Separation Algorithm for MIMO DSSS/CDMA System

Direct-sequence spread-spectrum (DSSS)/code division multiple access (CDMA) transmissions are now widely used for secure communications and multiple access. They can be transmitted at a low signal-to-noise ratio, and have a low probability of interception and capture. How to obtain the original users' signal in a noncooperative context or estimate the spreading sequence in blind conditions is a very difficult problem. Most of the signal sources are assumed to be instantaneous mixtures. In fact, the received CDMA signals are linearly convoluted. A more complicated blind source separation (BSS) algorithm is required to achieve better source separation. In this paper, a new BSS algorithm is proposed for separating linearly convolved signals in CDMA systems when the mixture coefficients of the signal and channel response are totally unknown, but some knowledge about the temporal model does exist. This algorithm is based on minimizing the squared cross-output-channel-correlation criterion. The simulation results show the effectiveness of the algorithm in the blind detection of DS-CDMA signals.     

A Non-Linear Blind Source Separation Method Based on Perceptron Structure and Conjugate Gradient Algorithm

The linear mixing model has been considered previously in most of the researches which are devoted to the blind source separation (BSS) problem. In practice, a more realistic BSS mixing model should be the non-linear one. In this paper, we propose a non-linear BSS method, in which a two-layer perceptron network is employed as the separating system to separate sources from observed non-linear mixture signals. The learning rules for the parameters of the separating system are derived based on the minimum mutual information criterion with conjugate gradient algorithm. Instead of choosing a proper non-linear functions empirically, the adaptive kernel density estimation is used in order to estimate the probability density functions and their derivatives of the separated signals. As a result, the score function of the perceptron’s outputs can be estimated directly. Simulations show good performance of the proposed non-linear BSS algorithm.     

基于多点平均频率校正的多表面波长调谐移相干涉测量

为实现多表面干涉测量中强度叠加干涉信号的分离和相位解调,提出了一种基于频率校正的多表面波长移相干涉测量算法,可实现透明被测件各表面面形的同时重建。波长移相干涉技术可以根据各干涉谐波光程差(optical path difference, OPD)的不同使各表面干涉谐波具有不同的移相值,该差异为各信号分离和相位解调提供了基础。在现有的多表面测量技术中,往往通过被测件的腔长和光学厚度等信息对谐波频率进行粗估,但估计精度较低,且无法应对移相误差。因此,本文通过多点平均和频率校正实现了各干涉谐波频率的精确提取,能够有效消除异常值和加性高斯噪声(additive Gaussian noise, AGN)对频率求解精度的影响,并且仅通过干涉图之间的加权操作便可同时对各谐波相位进行解调,对比分析和实验结果验证了所提出的算法的可靠性。     

Blind Signal Separation Using Steepest Descent Method

A method that significantly improves the convergence rate of the gradient-based blind signal separation (BSS) algorithm for convolutive mixtures is proposed. The proposed approach is based on the steepest descent algorithm suitable for constrained BSS problems, where the constraints are included to ease the permutation effects associated with the convolutive mixtures. In addition, the method is realized using a modified golden search method plus parabolic interpolation, and this allows the optimum step size to be determined with only a few calculations of the cost function. Evaluation of the proposed procedure in simulated environments and in a real room environment shows that the proposed method results in significantly faster convergence for the BSS when compared with a fixed step-size gradient-based algorithm. In addition, for blind signal extraction where only a main speech source is desired, a combined scheme consisting of the proposed BSS and a postprocessor, such as an adaptive noise canceller, offers impressive noise suppression levels while maintaining low-target signal distortion levels.     

基于EMD和ICA的单通道语音盲源分离算法

针对单通道语音信号盲分离的问题,结合盲源分离和经验模式分解的优点．提出了一种基于经验模式分解的单通道语音信号源数估计和盲源分离方法。对语音混合信号进行经验模式分解,利用贝叶斯算法估计语音源数目,根据源信号数目重组多通道语音混合信号,并采用独立分量分析实现语音信号的盲分离。仿真实验表明,使用此法能有效地估计通道语音信号源数和分离盲源。     

Method for eliminating mode mixing of empirical mode decomposition based on the revised blind source separation

Since mode mixing of empirical mode decomposition (EMD) is mainly caused by the intermittence and noise, we propose a novel method to eliminate mode mixing of EMD based on the revised blind source separation. To this aim, an optimal morphological filter is employed to eliminate the noise. As a result, the component of mode mixing caused by noise is suppressed. Furthermore, the de-noised signal is decomposed into different intrinsic mode function (IMF) components through the EMD algorithm. Since it is impossible to apply blind source separation to a single channel signal directly, the IMF component, which has mode mixing is chosen and reconstructed in the phase space. Following that, the equivalent hypothetical signals are obtained. Finally, an improved fixed-point algorithm based on independent component analysis (ICA) is introduced to separate the overlapping components. The analysis of simulation and practical application demonstrates that our proposed method can effectively tackle the mode mixing problem of EMD.     

扩展信息最大化盲源分离算法的研究

盲源分离技术是信号处理和神经网络领域近年来的一个热点研究课题,由于其能够从观测的混合信号中恢复出源信号,而对源信号和混合系统的先验知识要求很少,因此在语音信号处理、无线信号处理、生物医学信号处理、地震信号处理,以及图像增强等方面都具有非常重要的理论意义和实用价值。信息最大化盲源分离算法能够有效地分离语音信号的瞬时混合,但是不能分离超高斯信号(如语音信号)和亚高斯信号(如正弦信号)的混合。基于此,本文讨论了扩展信启、最大化盲源分离算法,通过仿真表明,该算法可以有效的对各种源信号的线性即时混合进行分离,实验证明了该算法的有效性。     

MAXIMUM LIKELIHOOD SOURCE SEPARATION FOR FINITE IMPULSE RESPONSE MULTIPLE INPUT—MULTIPLE OUTPUT CHANNELS IN THE PRESENCE OF ADDITIVE NOISE

Blind identification-blind equalization for finite Impulse Response(FIR)Multiple Input-Multiple Output(MIMO)channels can be reformulated as the problem of blind sources separation.It has been shown that blind identification via decorrelating sub-channels method could recover the input sources.The Blind Identification via Decorrelating Sub-channels(BIDS)algorithm first constructs a set of decorrelators,which decorrelate the output signals of subchannels,and then estimates the channel matrix using the transfer functions of the decorrelators and finally recovers the input signal using the estimated channel matrix.In this paper,a new qpproximation of the input source for FIR-MIMO channels based on the maximum likelihood source separation method is proposed.The proposed method outperforms BIDS in the presence of additive white Garssian noise.     

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.     

基于小波与独立分量分析的语音信号分离算法研究

混合语音信号的分离是盲源分离的重要内容,也是信号处理领域中的一个难题.对含噪声的混合信号采用小波滤波对信号进行去噪预处理,再采用基于信息极大分离算法提取信号的独立分量.实验结果表明,与传统的滤波方法相比,该算法在消除噪声的同时,对其他信号的细节几乎没有破坏,能够很好地分离频率相同或者相近的语音信号,而且去噪性能也比传统的滤波方法好.     

Stationary points of a kurtosis maximization algorithm for blindsignal separation and antenna beamforming

Blind source separation has been the subject of extensive research. In particular, blind antenna beamforming is an effective signal separation technique for communication systems to combat co-channel interference. Among many potential candidate approaches, the simple constant modulus algorithm (CMA) has been widely studied and used in practice. The CMA is designed to capture and separate signals with negative kurtosis. However, when some signals have positive kurtoses, the CMA is unable to capture and separate these sources. We show that the kurtosis maximum algorithm (KMA) can capture signals with both the positive and negative kurtoses. Its global convergence proof is presented for noiseless systems with multiple signals sources and for systems with a single source and zero-kurtosis (such as Gaussian) additive noise