一种新的自适应步长ICA算法

学习速率的优选问题是自适应ICA算法中的一个重要问题。文中定义了描述信号分离状态的去相关性测度。以此为基础提出一种新的自适应步长选取算法,算法将信号分离过程分为3个阶段,在不同的阶段采用不同的步长选取方法。有效地解决了算法的稳态误差和收敛速度之间的矛盾,其仿真结果证实,该算法不仅在平稳环境中具有较快的收敛速度,而且具有良好的稳态性能和数值稳定性。     

瞬时梯度去相关在线EASI算法

针对在线EASI算法的瞬时梯度与真实梯度存在偏差,并且偏差会随着分离矩阵的迭代更新传递和累积下去,造成分离性能下降的问题,提出了一种瞬时梯度去相关的在线EASI算法—IGDA-EASI算法。IGDA-EASI算法通过消除瞬时梯度间的相关性,减小偏差累积,从而提高算法的分离性能。经仿真实验验证,该算法相较传统在线EASI算法在收敛速度和分离精度方面都获得了较大提高。在信道时变的情况下,IGDA-EASI算法同样具有更好的分离性能。并且在动量项EASI算法中IGDA算法依然具有适用性。      

改进的变步长维纳系统盲源分离方法

基于非线性盲源分离的维纳系统算法中,采用固定步长导致算法的收敛速度和稳态误差之间存在矛盾,直接影响分离算法的性能。为了解决该问题,提出了基于非线性函数的变步长维纳系统盲源分离方法。该方法将更新的步长以非线性函数的形式引入到分离算法中,使得稳态时参数更新的步长尽可能小,以避免发生振荡。变步长算法在分离过程中的每次更新都会使步长自动进行合理的调整,使得收敛速度提高了53%,误差减小了45%。实验仿真表明,相对原算法,提出的维纳系统盲源分离方法可以更好地分离出信源信号,而且具有较小的误差和较快的收敛速度。     

基于萤火虫算法的变步长语音信号盲源分离

针对传统的自然梯度算法对语音信号进行盲源分离时,算法存在收敛速度和稳态误差的矛盾。文中提出一种新的变步长自然梯度算法,利用荧火虫算法对自然梯度算法的步长调整,使算法的步长随信号的分离度变化,并通过计算机仿真验证了该算法的有效性,算法的分离效果更好,收敛速度更快,且稳态误差更小。     

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。     

A near real-time approach for convolutive blind source separation

In this paper, we propose an algorithm for real-time signal processing of convolutive blind source separation (CBSS), which is a promising technique for acoustic source separation in a realistic environment, e.g., room/office or vehicle. First, we apply an overlap-and-save (sliding windows with overlapping) strategy that is most suitable for real-time CBSS processing; this approach can also aid in solving the permutation problem. Second, we consider the issue of separating sources in the frequency domain. We introduce a modified correlation matrix of observed signals and perform CBSS by diagonalization of the matrix. Third, we propose a method that can diagonalize the modified correlation matrix by solving a so-called normal equation for CBSS. One desirable feature of our proposed algorithm is that it can solve the CBSS problem explicitly, rather than stochastically, as is done with conventional algorithms. Moreover, a real-time separation of the convolutive mixtures of sources can be performed. We designed several simulations to compare the effectiveness of our algorithm with its counterpart, the gradient-based approach. Our proposed algorithm displayed superior convergence rates relative to the gradient-based approach. We also designed an experiment for testing the efficacy of the algorithm in real-time CBSS processing aimed at separating acoustic sources in realistic environments. Within this experimental context, the convergence time of our algorithms was substantially faster than that of the gradient-based algorithms. Moreover, our algorithm converges to a much lower value of the cost function than that of the gradient-based algorithm, ensuring better performance.     

Blind signal separation for coprime planar arrays: An improved coupled trilinear decomposition method

In this study, the problem of blind signal separation for coprime planar arrays is investigated. For coprime planar arrays comprising two uniform rectangular subarrays, we link the signal separation to the tensor-based model called coupled canonical polyadic decomposition (CPD) and propose an improved coupled trilinear decomposition approach. The output data of coprime planar arrays are modeled as a coupled tensor set that can be further interpreted as a coupled CPD model, allowing a signal separation to be achieved using coupled trilinear alternating least squares (TALS). Furthermore, in the procedure of the coupled TALS, a Vandermonde structure enforcing approach is explicitly applied, which is shown to ensure fast convergence. The results of Monto Carlo simulations show that our proposed algorithm has the same separation accuracy as the basic coupled TALS but with a faster convergence speed.     

Block-Shanno multi-user constant modulus algorithm for use inmulti-user CDMA environment

In a multi-user direct sequence code division multiple access (DS-CDMA) system, retrieval of transmitted signals with a conventional receiver is difficult when there is chip asynchronism, multipath propagation and the associated near-far problem. Results presented show that despite these problems an adaptive linear receiver based on a mixed cross correlation and constant modulus algorithm (CC-CMA) has the potential to retrieve all users simultaneously. Compared with the conventional CC-CMA algorithm, the proposed algorithm achieves faster convergence. Simulations support the improved convergence properties of the algorithm     

A family of adaptive filter algorithms with decorrelatingproperties

Although the normalized least mean square (NLMS) algorithm is robust, it suffers from low convergence speed if driven by highly correlated input signals. One method presented to overcome this problem is the Ozeki/Umeda (1984) affine projection (AP) algorithm. The algorithm applies update directions that are orthogonal to the last P input vectors and thus allows decorrelation of an AR(P) input process, speeding up the convergence. This article presents a simple approach to show this property, which furthermore leads to the construction of new algorithms that can handle other kinds of correlations such as MA and ARMA processes. A statistical analysis is presented for this family of algorithms. Similar to the AP algorithm, these algorithms also suffer a possible increase in the noise energy caused by their pre-whitening filters     

Linear adaptive decorrelator for signal separation

Signal separation is a problem often encountered in many practical applications. A novel technique is described that can guarantee signal separation by output decorrelation. In particular, it is shown that a simple transformation of the variables enables the nonlinear set of equations to be solved efficiently using the standard least squares technique. Moreover, the uniqueness of the solution is analytically determined and the system is shown to guarantee separation. The algorithm is modified to estimate the parameters adaptively, and the results clearly show the improved performance of this algorithm in separating two signals     

Blind separation algorithm without nonlinear functions

A new blind separation algorithm without nonlinear functions is proposed. The algorithm is derived using the decorrelation process near an equilibrium point. The algorithm uses the correlation between its outputs and the sensor signals in the decorrelation process. The validity and performance of the algorithm are confirmed through computer simulations     

AN IMPROVED FAST BLIND DECONVOLUTION ALGORITHM BASED ON DECORRELATION AND BLOCK MATRIX

In order to alleviate the shortcomings of most blind deconvolution algorithms,this paper proposes an improved fast algorithm for blind deconvolution based on decorrelation technique and broadband block matrix. Althougth the original algorithm can overcome the shortcomings of current blind deconvolution algorithms,it has a constraint that the number of the source signals must be less than that of the channels. The improved algorithm deletes this constraint by using decorrelation technique. Besides,the improved algorithm raises the separation speed in terms of improving the computing methods of the output signal matrix. Simulation results demonstrate the validation and fast separation of the improved algorithm.     

改进的信息最大化语音盲分离算法

提出一种改进的基于信息最大化的语音盲分离算法,克服了以往算法收敛速度慢,串音误差大的缺点.新算法分析了多种非线性函数的转换特性,采用一种新的更合适的非线性函数,并依此推导出分离算法的学习规则.实验表明,改进的算法有效实现了混叠语音信号的盲源分离,收敛速度更快,串音误差更小,取得了良好、稳定的分离效果.     

An improved fast blind deconvolution algorithm based on decorrelation and block matrix1

In order to alleviate the shortcomings of most blind deconvolution algorithms, this paper proposes an improved fast algorithm for blind deconvolution based on decorrelation technique and broadband block matrix. Althougth the original algorithm can overcome the shortcomings of current blind deconvolution algorithms, it has a constraint that the number of the source signals must be less than that of the channels. The improved algorithm deletes this constraint by using decorrelation technique. Besides, the improved algorithm raises the separation speed in terms of improving the computing methods of the output signal matrix. Simulation results demonstrate the validation and fast separation of the improved algorithm.     

基于BFGS方法的拥塞速率控制算法

针对TCP/AQM对偶性模型采用梯度投影方法调整链路价格收敛速度慢的问题,使用具有更快收敛速度的BFGS方法来进行链路价格的计算,提出一种基于BFGS方法的拥塞速率控制算法。仿真结果证明,利用BFGS方法所设计拥塞速率控制算法具有更快的收敛速度,算法性能优于其它算法。     

同伦BP网络理论与算法

ＢＰ网络和算法是使用最广泛的神经网络模型之一，但由于它使用梯度算法，因而存在有固有的局域极小及收敛速度慢等问题。本文首先把一ＢＰ网络的优化问题变换为一非线性方程的求解，然后将同伦思想引入到神经网络学习训练之中，提出了相应的同伦ＢＰ网络理论与算法，它具有全局收敛的优点，同时具有比传统梯度法收敛速度快一个数量级以上和克服病态的能力，在ＢＰ梯度法不收敛时它也能给出正确解答。本文的理论证明和计算机仿真实验     

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.     

Optimal radio channel assignment through a new binary dynamic simulated annealing algorithm

The radio channel assignment problem (CAP) is classified as an NP-complete binary optimization problem, which creates the need for faster, yet optimal optimization algorithms to reduce the time of computation when solving such a complex problem. Simulated annealing (SA), a powerful optimal combinatorial search algorithm, was found to be very suitable for CAP. This paper extends the standard capabilities of SA and proposes a new CAP-oriented, quicker binary SA, the binary dynamic SA (BDSA) algorithm, as part of a newly proposed radio channel assignment approach. Simulation results proved that the proposed BDSA has very fast convergence as a stand-alone algorithm and even faster convergence with the newly proposed radio channel assignment approach. © 1998 John Wiley & Sons, Ltd.     

A Least Squares Approach to Escalator Algorithms for Adaptive Filtering

In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization property of the ESC structure. From the simulation results, the proposed algorithm shows superior convergence performance.     

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.