超定盲信号分离RLS算法研究

研究在线盲信号分离问题.将适定盲信号分离的RLS算法,推广到源信号个数未知的超定盲信号分离模型中,利用正交投影方法消除了超定盲信号分离RLS算法的冗余移动,设计出能够稳定收敛的超定盲信号分离RLS算法.仿真实验验证了算法的有效性和其收敛的稳定性.     

粒子群算法用于盲信号分离的研究

提出一种采用粒子群优化算法进行盲信号分离的新方法,为盲信号分离领域提供一种新的研究思路与方法。该方法采用峰度作为适应度函数,利用粒子群算法对由多个源信号混合而成的信号进行盲信号分离。与自然梯度法盲信号分离相比,粒子群算法精度更高,收敛速度更快,实例仿真成功地对两个图像混合信号进行了盲分离,表明了算法的有效性和优越性。     

基于盲信源分离技术的雷达信号分选研究

基于雷达信号分选的实时性处理要求，提出了一种基于盲信源分离（BSS，Blind Source Sepamtion）技术的雷达信号分选算法。该算法在盲信源分离自然梯度算法的基础上，引入动态神经网络（DNN）构成分离系统，通过自适应增加或删减输出神经元个数，检测出变化着的雷达信号数目，并很好的对其进行分离。其中，增加输出神经元个数的方法很好的解决了脉冲雷达信号并非同时到达时的问题。仿真结果验证了该算法的有效性。     

源信号数目未知正交投影自然梯度盲分离算法

分析了源信号数目未知的盲信号分离算法中分离矩阵范数趋于无穷进而导致算法发散的原因。利用正交投影自然梯度方法消去了自然梯度中引起冗余移动的冗余分量,推导出源信号数目未知的正交投影自然梯度算法。新算法具有与自然梯度算法相同的收敛速度,且克服了已有算法不能稳定收敛的缺点。仿真验证了新算法的分离性能和收敛稳定性。     

源信号数目未知的自然梯度盲信号分离算法

总结了源信号数目未知的盲信号分离自然梯度算法,得到自然梯度算法发散的原因,分离矩阵的各行沿混合矩阵转置的零空间方向无效的冗余移动。借助投影自然梯度算法,从理论上证明,冗余分量的范数随迭代次数的增加呈指数分布。     

盲信号分离及盲信号抽取研究

盲信号分离技术是将混合信号中的源信号分离出来的一种功能强大的信号处理方法,已成为信号处理领域的研究热点。阐述了盲信号分离的发展现状,介绍了盲信号分离问题的数学模型,给出了盲源分离的基本思想。对盲信号分离算法进行了研究,阐述了盲信号分离几种典型算法的特点及性能,对与盲信号分离紧密相关的盲信号抽取算法进行了总结,并对盲信号分离的进一步研究进行了展望。     

基于峰度自然对数最大化与奇异值分解的超定盲信号分离

提出了一种多个信号源的超定盲信号分离算法,该方法利用奇异值分解来确定信号源的个数,并把天线阵的接收数据影射到正交的信号子空间中进行降维处理,再通过峰度自然对数最大化准则,对多个信号源按峰度减少的顺序依次进行分离.学习速率用非线性函数进行调节,避免了人为选取不当而导致的算法发散.该算法收敛速度快,且有较强的稳健性.计算机仿真验证了算法的有效性.     

基于信源数目估计的超定盲信号分离

针对盲信号分离中信道噪声大、信号分离效果差等问题,在传统主分量分析和特征值分解方法的基础上,提出了一种基于信源数目估计的超定盲信号分离方法。首先,采用主分量分析和最大似然估计方法分别对混合矩阵和噪声协方差进行估计,用于对信道噪声的估计与去除;然后,采用交叉验证法对源信号维数进行估计,实现盲信号分离。为了验证提出算法的分离效果,对轻拖尾与轻拖尾混合信号以及重拖尾与轻拖尾信号混合情况进行仿真实验验证,结果表明该算法具有良好的分离效果。     

超定盲信号分离的半参数统计方法

研究观测信号的数目m不小于源信号的数目n情况下盲信号分离问题.首先证明若混合矩阵满列秩,则在本质相等意义下,存在唯一的m×m非奇异矩阵使得分离系统的输出除零信号外,其它非零信号即是希望提取的源信号.基于此,采用半参数统计方法构造超定盲信号分离的估计函数,给出相应的学习算法;理论证明了该算法具有等变化性和分离矩阵的非奇异特性,并借助于源信号数目未知且动态变化的计算机仿真验证了其有效性.     

欠定盲信号抽取算法研究

欠定盲信号分离技术是信号处理领域发展相对较晚的一种理论，目前已迅速成为该领域内重要的组成部分。本文首先对欠定盲信号的基本研究方法和分离问题的数学模型作了介绍，详细介绍了欠定盲信号的抽取算法，并指出其主要特点和性能。     

分离矩阵的步长自适应在线盲源分离算法

分析了超定盲源分离中的自然梯度算法最终不能稳定收敛的原因,针对解决这一问题的方法中存在的不足进行了分析和研究。采用了一种基于分离矩阵的步长自适应在线盲源分离算法,较好地实现了收敛速度与稳态误差的最优结合。同时,在信号随机减少或增加时改进算法也能够达到较好的分离效果,仿真结果验证了改进算法的收敛稳定性与分离有效性。     

A SIGNAL-ADAPTIVE ALGORITHM FOR BLIND SEPARATION OF SOURCES WITH MIXED KURTOSIS SIGNS

This paper addresses the problem of Blind Source Separation （BSS） and presents a new BSS algorithm with a Signal-Adaptive Activation （SAA） function （SAA-BSS）. By taking the sum of absolute values of the normalized kurtoses as a contrast function, the obtained signal-adaptive activation function automatically satisfies the local stability and robustness conditions. The SAA-BSS exploits the natural gradient learning on the Stiefel manifold, and it is an equivariant algorithm with a moderate computational load. Computer simulations show that the SAA-BSS can perform blind separation of mixed sub-Gaussian and super-Gaussian signals and it works more efficiently than the existing algorithms in convergence speed and robustness against outliers.     

不同幅度通信信号的盲源分离

自然梯度算法是盲源分离的重要方法。本文利用自然梯度算法研究不同幅度的独立信源线性混合的盲源分离问题。我们比较了各种活动函数并提出一个更好的活动函数。由10个强弱不同的通信信号作为信源进行了模拟实验,结果显示用该活动函数算法收敛得更快些。     

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.     

Adaptive nonlinear PCA algorithms for blind source separation without prewhitening

Blind source separation (BSS) aims at recovering statistically independent source signals from their linear mixtures without knowing the mixing coefficients. Besides independent component analysis, nonlinear principal component analysis (NPCA) is shown to be another useful tool for solving this problem, but it requires that the measured data be prewhitened. By taking into account the autocorrelation matrix of the measured data, we present in this paper a modified NPCA criterion, and develop a least-mean-square (LMS) algorithm and a recursive least-squares algorithm. They can perform the online BSS using directly the unwhitened observations. Since a natural gradient learning is applied and the prewhitening process is removed, the proposed algorithms work more efficiently than the existing NPCA algorithms, as verified by computer simulations on man-made sources as well as practical speech signals.     

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。     

独立分量分析可调速率相对梯度算法

在独立分量分析的相对梯度算法中,要取得较好的效果,选取合适的学习速率是至关重要的。对于这个问题,文章提出了一种可调速率的相对梯度算法,随着迭代次数的变化,使相对梯度算法的学习速率作相应变化,从而较好地解决了收敛速度与稳定性的矛盾。在此基础上,将这个方法应用于盲信号分离并进行仿真,得到了满意的结果。可调速率相对梯度算法在独立分量分析中具有较好的前景。     

Learning Behaviors of Stochastic Gradient Radial Basis Function Network Algorithms for Odor Sensing Systems

Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF‐SVD‐SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine‐tuning of centers and widths still shows ill‐behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center‐gradient variance of the RBFN‐SVD‐SG algorithm. We found analytically that the steady‐state weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center‐gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady‐state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.     

Blind-Source Separation Based on Decorrelation and Nonstationarity

In this paper, discrete-time blind-source separation (BSS) of instantaneous mixtures is studied. Decorrelation-based sufficient criteria for BSS of stationary and nonstationary sources are derived based on nonstationarity and nonwhiteness. A gradient algorithm is proposed based on these criteria. A batch-data algorithm and an on-line algorithm are developed based on the corollaries of the BSS criteria. These algorithms are especially useful for the separation of nonstationary sources. They are robust to additive white noises if the time-delayed decorrelation and the nonstationarity of the sources are considered simultaneously in the algorithms. Experiment results show the effectiveness and performance of the proposed algorithms     

Subspace-Based Channel Shortening for the Blind Separation of Convolutive Mixtures

A novel subspace-based channel shortening procedure is proposed based on the structure of the delayed autocorrelation matrices of the observation process. This purely second-order approach applies to overdetermined multiple-input multiple-output (MIMO) channels with independent, white sources. The channel may be sparse, and its length is assumed to be unknown. Through successive deflations, the problem can be transformed into an instantaneous blind source separation (BSS) problem which is simpler to solve using, for example, independent component analysis (ICA) techniques. The algorithm is computationally fast although it requires large input datasets. Such data can be acquired either through large numbers of sensors or by using increased data sampling rate. When not enough data are available, the method can still be used for reducing the channel length thus simplifying the problem for subsequent treatment.