Zero‐attracting variable‐step‐size least mean square algorithms for adaptive sparse channel estimation

Recently, sparsity‐aware least mean square (LMS) algorithms have been proposed to improve the performance of the standard LMS algorithm for various sparse signals, such as the well‐known zero‐attracting LMS (ZA‐LMS) algorithm and its reweighted ZA‐LMS (RZA‐LMS) algorithm. To utilize the sparsity of the channels in wireless communication and one of the inherent advantages of the RZA‐LMS algorithm, we propose an adaptive reweighted zero‐attracting sigmoid functioned variable‐step‐size LMS (ARZA‐SVSS‐LMS) algorithm by the use of variable‐step‐size techniques and parameter adjustment method. As a result, the proposed ARZA‐SVSS‐LMS algorithm can achieve faster convergence speed and better steady‐state performance, which are verified in a sparse channel and compared with those of other popular LMS algorithms. The simulation results show that the proposed ARZA‐SVSS‐LMS algorithm outperforms the standard LMS algorithm and the previously proposed sparsity‐aware algorithms for dealing with sparse signals. Copyright © 2015 John Wiley & Sons, Ltd.     

Selective partial update and set‐membership improved proportionate normalized subband adaptive filter algorithms

In this article, the concept of proportionate adaptation is extended to the selective partial update (SPU) and set‐membership (SM) normalized subband adaptive filters (NSAFs), and three proportionate normalized subband adaptive filter algorithms are established. The proposed algorithms are the improved proportionate NSAF (IPNSAF), the SPU improved proportionate NSAF (SPU‐IPNSAF), and the SM‐IPNSAF which are suitable for sparse system identification. When the impulse response of the echo path is sparse, the IPNSAF algorithm has faster convergence than NSAF. The performance of IPNSAF is also suitable for dispersive impulse responses. In SPU‐IPNSAF, the filter coefficients are partially updated rather than the entire filter at every adaptation which reduces the computational complexity of IPNSAF. The SM‐IPNSAF exhibits good performance with significant reduction in the overall computational complexity compared with the ordinary IPNSAF. The simulation results show good performance of the proposed algorithms. Copyright © 2010 John Wiley & Sons, Ltd.     

A simple variable step size LMS adaptive algorithm

A new LMS based variable step size adaptive algorithm is presented. The step size is incremented or decremented by a small positive value, whenever the instantaneous error is positive or negative, respectively. The algorithm is simple, robust and efficient. It is characterized by fast convergence and low steady state mean squared error. The performance of the algorithm is analysed for a stationary zero‐mean white‐Gaussian input. MC simulations are provided to demonstrate its improved performance over the conventional LMS (Proc. IEEE 1976; 64 :1151–1162) and some other variable step size adaptive algorithms (IEEE Trans. Signal Process. 1992; 40 :1633–1642; IEEE Trans. Signal Process. 1997; 45 :631–639) within a range of statistical environments. For a non‐stationary input, the proposed algorithm behaves similar to these algorithms. A modified version of the algorithm is presented to perform in the presence of abrupt changes. Copyright © 2004 John Wiley & Sons, Ltd.     

A new two‐microphone Gauss‐Seidel pseudo affine projection algorithm for speech quality enhancement

This study addresses the problem of speech quality enhancement by adaptive and nonadaptive filtering algorithms. The well‐known two‐microphone forward blind source separation (TM‐FBSS) structure has been largely studied in the literature. Several two‐microphone algorithms combined with TM‐FBSS have been recently proposed. In this study, we propose 2 contributions: In the first, a new two‐microphone Gauss‐Seidel pseudo affine projection (TM‐GSPAP) algorithm is combined with TM‐FBSS. In the second, we propose to use the new TM‐GSPAP algorithm in speech enhancement. Furthermore, we show the efficiency of the proposed TM‐GSPAP algorithm in speech enhancement when highly noisy observations are available. To validate the good performances of our algorithm, we have evaluated the adaptive filtering properties in computational complexity and convergence speed performance by system mismatch criteria. A fair comparison with adaptive and nonadaptive noise reduction algorithms are also presented. The adaptive algorithms are the well‐known two‐microphone normalized least mean square algorithm, and the recently published two‐microphone pseudo affine projection algorithm. The nonadaptive algorithms are the one‐microphone spectral subtraction and the two‐microphone Wiener filter algorithm. We evalute the quality of the output speech signal in each algorithm by several objective and subjective criteria as the segmental signal‐to‐noise ratio, cepstral distance, perceptual evaluation of speech quality, and the mean opinion score. Finally, we validate the superior performances of the proposed algorithm with physically measured signals.     

基于LMS算法自适应噪声抵消系统的仿真研究

介绍了自适应噪声抵消系统的原理和LMS算法,并给出了基于LMS算法自适应噪声抵消系统的仿真图。通过分析仿真结果表明基于LMS算法的自适应噪声抵消技术可以有效的抵消正弦干扰信号,这样在语音信号处理中就能消除含噪语音信号中的背景噪音,达到提高语音信号质量的目的。     

基于RLS自适应滤波算法的电力有源滤波器直流端电压控制方法

RLS自适应滤波器用于有源滤波器直流端电容电压的控制,克服了用LMS自适应滤波器收敛速度慢和稳态误差较大的缺点,同时对直流端电容电压的控制完全摒弃了传统的PI积分控制,解决了控制参数难以调节及对电路中其他参数的变化非常敏感的问题.稳态和暂态特性的仿真研究比较了两种自适应滤波算法对直流端电容电压的控制及对补偿后的主电流的影响,其中RLS自适应滤波器效果要好得多.     

基于RLS算法的时变谐波检测

以自适应线性组合器为时变谐波检测器的模型,根据逆归最小=乘(RLS)自适应滤波算法较好的跟踪性能,使之应用于时变谐波的跟踪检测.仿真表明该方法比以往的基于最小均方(LMS)自适应滤波算法的谐波幅值和相位参数的测定具有更好的跟踪效果.     

横向时延滤波和IQ正交滤波的滤波效果仿真

由于横向时延滤波和IQ正交滤波在稳定性、收敛速度和误差率等方面的性能差异对于最小均方算法(LMS,Least Mean Squares)在工程上的实际应用具有参考意义,采用数值仿真的方法模拟了两者实现自适应最小均方算法的具体过程,并对比分析了它们的滤波性能。仿真结果显示IQ正交滤波具有更高的稳定性、更快的收敛速度、更小的误差率,其滤波性能优于横向时延滤波。IQ正交滤波器的阶数为2阶,而横向时延滤波器的阶数通常>10,滤波器阶数越多引入的权噪声也会相对增加,对于稳定性、收敛速度和误差率都是不利的,因此实际应用中优先选择阶数较少的IQ正交滤波器。     

基于LMS算法的热工过程建模研究

热工过程往往表现出非线性、大惯性和大迟延等特点,不易建立精确的数学模型,难以精确表达热工过程以至于不能达到理想的控制效果。针对这一问题,提出了基于LMS算法的自适应滤波建模方法。该方法通过误差信号自适应调整每个抽头权值,使基于该算法的自适应滤波器能够迅速的收敛到要求的输入和输出关系,从而达到精确建模的目的。仿真结果表明该算法复杂度低,精度高,能有效的应用于热工过程建模。     

基于自适应算法的谐波检测方法研究

由于低通滤波器的影响,传统的ip-iq算法有检测速度慢的缺点,针对此缺点,本文提出了一种基于变步长LMS/LMF算法自适应滤波器来代替低通滤波器的作用,针对输入信号的特点相应的改变自适应滤波器的步长因子,同时改变最小均方算法(LMS)和最小四阶矩算法(LMF)的比例,充分发挥了LMF和LMS检测精度和检测速度的优势。仿真结果表明,提出的算法检测速度和精度具有明显的优势,具有一定的工程应用价值。     

一种新的变步长LMS自适应算法及其应用

传统的固定步长的LMS算法难于同时获取较快的收敛速度与较小的稳态误差,基于这一矛盾,本文在分析基本LMS算法的基础上,提出了一种新的变步长LMS算法,该算法具有较快的收敛速度和较小的稳态误差。理论分析和计算机仿真结果表明该算法在收敛速度与稳态误差的性能上均优于基本LMS算法。尤其是在低估噪比的情况下,其性能的优越性更为突出。与传统的LMS算法相比较,新算法更适合于应用到回波消除等实时性要求高的场合。     

基于带双窗N-1/N重叠频域FIR滤波器的自适应除噪

基于一种带双窗重叠自适应滤波器,将重叠滤波思想引入LMS算法,给出了窗加权重叠LMS(WO-LMS)算法.与传统的LMS算法相比,WO-LMS算法在同样收敛速度的前提下可以得到较低的稳态均方误差.理论分析了算法的收敛性,实验中通过和LMS算法的比较,验证了WO-LMS算法的优越性.     

一种改进ip-iq谐波电流检测算法

为了更加快速、精确地检测出三相电力系统中谐波电流,基于瞬时无功功率理论,提出了一种改进谐波电流检测算法.该算法利用一种变步长LMS算法来实现ip-iq理论中低通滤波器的功能,用当前误差信号和上一次误差信号归一化的自相关估计来进行步长迭代.与传统ip-iq算法相比,该算法在不降低检测精度的前提下,具有更快的动态响应性能....     

A dual filtering scheme for nonlinear active noise control

Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered‐x least mean squares (LMS) or filtered‐u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness. Copyright © 2013 John Wiley & Sons, Ltd.     

基于LMS算法的自适应滤波在负压波信号噪声处理中的应用

本文以负压波信号采集滤波为例,介绍了基于LMS算法的自适应滤波器的基本原理和该滤波器几个关键参数设定的基本方法。实践证明,在采集随机不确定信号时,采用此滤波器进行信号调理具有比传统滤波器不可比拟的优越性。     

基于自感知执行器原理的调谐滤波器

根据自感知执行器的工作原理,利用一只变压器完成谐波检测和抑制两种功能,处于正常工作状态时可实现谐波抑制,处于非正常工作状态时不致放大谐波。首先对工频信号进行数字采样,再利用自相关函数法结合递推最小二乘估计工频信号的频率,再根据该频率产生反向抑制谐波,来抑制当前谐波。为实时调整各次反向抑制谐波的权重,根据当前时刻的谐波抑制的结果,采取最小均方自适应算法来更新权重。模拟试验表明,该调谐滤波器具有良好的控制特性。     

并联式多级回声抵消器设计研究

在回声抵消研究中,LMS算法由于具有算法简单、易于实现的特点而得到广泛应用.然而,采用LMS算法的自适应回声抵消器的性能仍然有很大的改进余地,可以通过对算法本身的改进和对利用该算法的自适应滤波器的结构的改进达到更好的性能,主要研究了利用LMS算法的AEC自适应滤波器结构上的改进,提出了一种新的滤波器设计方法,该方法通过将采样信号在输入端拆分,分别输入到多个并联的子滤波器中分别处理,得到对回声信号的估计后将它从掺杂有回声信号的接收信号中消除,并在输出端重新组合,成为对原始信号的恢复.这种并联多通道结构可以有效提高回声抵消器的收敛速度、降低运算量.由于这种方法利用了多通道并联自适应滤波器的结构,因此要求非常严格的数据同步和时间控制.仿真结果表明,这种方法对回声抵消具有良好的效果.     

Filter proportionate normalized least mean square algorithm for a sparse system

In this paper, the proportionate normalized least mean square (PNLMS) and its modifications, such as improved PNLMS (IPNLMS) and μ‐law PNLMS (MPNLMS) algorithms, developed for a sparse system, are analyzed for a compressed input signal. This analysis is based on a comparative study of the steady‐state error and convergence time for the original signal and the compressed signal. Further, in this paper, a filter PNLMS (FPNLMS) algorithm that is a modification of the IPNLMS algorithm is proposed. The FPNLMS algorithm uses a step size varying in time to adapt to the sparse system. Simulations are carried out to compare the proposed FPNLMS algorithm for different signal‐to‐noise ratio for a compressed input signal with existing algorithms, ie, PNLMS, MPNLMS, and IPNLMS algorithms. The FPNLMS algorithm achieves a better steady‐state and convergence time compared with other existing algorithms in both low and high SNRs. The FPNLMS algorithm is further simulated for a real transfer function to show its robustness compared with existing algorithms.     

基于自适应滤波前馈控制的结构振动抑制研究

采用自适应滤波前馈方法对压电智能结构悬臂梁进行振动控制。分别研究了常用LMS自适应算法及其改进型NLMS算法在振动主动控制中的应用,并对两种算法的性能进行了比较。理论分析和仿真结果表明,自适应滤波前馈控制能够有效的控制悬臂梁的振动,基于NLMS算法比基于LMS算法的前馈控制具有更快的响应速度和更小的稳态误差。     

基于小波变换和LMS自适应滤波器的单相接地选线方法

综合应用小波变换理论和最小均方误差(LMS)自适应滤波器技术,提出了一种小电流接地系统单相接地故障选线方法.该方法对故障发生后的零序电流信号进行小波变换,通过LMS自适应滤波器对小波系数进行滤波去噪,利用去噪后的信息进行选线.该方法能有效地从被噪声干扰的信号中得到有用的信息,克服了故障信号信噪比低的缺点,并能够充分利用故障信息连续判断.应用现场故障录波数据对该方法进行了验证,结果表明该方法能够满足实用要求.