一种变步长凸组合自适应滤波器及其均方性能分析

在分析凸组合最小均方(CLMS)算法性能的基础上,提出一种新的变步长凸组合最小均方(VSCLMS)算法。该算法采用一种变步长滤波器替代原CLMS滤波器组中的恒值大步长滤波器,使新的自适应滤波器能够在噪声、时变,甚至非平稳的环境下保持良好的随动性能,并在收敛的各个阶段均保持快速且稳定的均方特性。理论推导与仿真分析分别验证了新算法与原CLMS算法相比不仅有更快的收敛速度,而且稳态均方性能与跟踪性能也有所提高。     

一种变步长凸组合LMS自适应滤波算法改进及分析

为了避免单个滤波器在收敛速度与稳态误差上相互制约,从而导致系统性能降低的问题,本文采用凸组合最小均方算法（Combined Least Mean Square ,CLMS ）,将快速滤波器和慢速滤波器并联使用,同时为进一步改善CLMS算法的性能,对已有的变步长凸组合最小均方算法（Variable Step-size Convex Combination of LMS ,VSCLMS ）做出改进,提出了一种新的VSCLMS算法。在该算法中,对快速滤波器选用以最小均方权值偏差（Minimization of Mean Square Weight Error ,MMSWE）为准则的按步分析的变步长滤波器;对慢速滤波器采用以稳态最小均方误差（Least Mean Square , LMS ）为准则的固定步长滤波器。通过理论分析与仿真实验表明,该算法能够在噪声、时变以及非平稳的环境下保持较好的随动性能,且在各个阶段均保持良好的收敛性,与传统的CLMS、VSCLMS算法相比,不仅具有更快的收敛速度,而且拥有稳定的均方性能和较优的跟踪性能,为自适应滤波算法的研究提供了一条可行途径。     

自适应回声消除算法的仿真研究

介绍几种自适应回声消除算法:NLMS,PNLMS,PNLMSDU,IPNLMS,MPNLMS,并在Matlab环境下进行了仿真比较。采用高斯噪声作为输入,分别在回声路径稀疏、回声路径中等稀疏、回声路径完全非稀疏的条件下进行仿真。仿真结果表明,PNLMS算法是针对稀疏回声路径的有效快速算法,MPNLMS算法做了进一步的优化。IPN-LMS算法和PNLMSDU算法融合了NLMS算法和PNLMS算法的优点,既保证有较快的初始收敛速度,又保证后阶段收敛速度不明显下降。     

等效凹槽滤波器及其在无源相关定位雷达中的应用(英文)

归一化最小均方误差(NLMS)算法被广泛应用于无源相干定位(PCL)雷达系统的直达波和多径干扰对消。该文提出NLMS干扰对消器与雷达模糊函数结合可以等效为凹槽滤波器,该滤波器在雷达模糊函数平面中的零多普勒处产生一个凹槽。分析显示凹槽的宽度和深度与NLMS算法的步长密切相关。文章分析了凹槽对PCL雷达目标检测的影响,结果显示宽的凹槽会使PCL雷达系统的目标检测性能恶化。文章进一步提出了非均匀归一化最小均方误差(Non-uniform NLMS, NNLMS)算法,该算法能有效抑制具有多普勒频率的杂波,并且能有效降低雷达模糊函数的底噪。该算法引进了步长矩阵,利用该矩阵可以实现在不同的距离单元产生不同宽度的凹槽,每个距离门的凹槽宽度取决于杂波干扰的能量和多普勒频率。与传统NLMS相比,NNLMS算法可以实现更快的收敛速度,试验结果验证了该算法的有效性及优越性。      

时变参数比例自适应滤波算法

在免提电话和视频会议系统中,自适应滤波器估计的回声路径通常是稀疏的.改进的比例归一化最小均方(IPNLMS)算法能够加快自适应滤波器在估计稀疏系统时的收敛速度,但与归一化最小均方(NLMS)算法相比,其稳态失调的波动性较大.为了解决这一问题,本文提出了一种时变参数IPNLMS(TV-IPNLMS)算法.该算法根据系统的均方误差(MSE)与噪声功率的比值,使用一个sigmoid函数来调整时变参数的值.该时变参数能够降低IPNLMS算法在滤波器到达稳态时的比例增益.仿真结果表明,时变参数方法能够降低IPNLMS算法稳态失调的波动性.该算法可用于回声消除、主动噪声控制等领域.     

一种新的可变步长LMS自适应滤波算法及其算法分析

在讨论基本LMS．变步长NLMS和LMS／F组合自适应滤波算法的基础上提出一种新的可变步长LMS自适应滤波算法,新算法引入修正系数和遗忘因子．并利用和来产生新的步长参与迭代。计算机仿真结果表明,与基本LMS算法或变步长NLMS、LMS／F组合算法相比,新算法在保持算法简单这一特点的同时进一步加快了收敛速度,并能够收敛到更小且稳定的均方误差(MSE)。     

基于LMS的变步长自适应凸联合算法

针对传统固定步长凸联合最小均方算法动态跟踪性能差,初始时刻小步长组成滤波器收敛速度慢和两个组成滤波器独立迭代的缺点,本文提出一种新的变步长凸联合最小均方滤波算法,充分利用组成滤波器的自身经验及全局信息,将组成滤波器步长及联合步长设计为变步长,可根据环境变化进行动态调整,提高了算法对时变系统的适应能力。理论分析和计算机仿真实验表明,新算法在收敛速度,稳态性能和跟踪能力方面都优于传统的固定步长凸联合最小均方算法。     

F-R共轭梯度算法在自适应滤波器中的应用

在自适应滤波器设计过程中,采用传统的LMS算法,具有计算简单、易于实现等优点,但是该算法的缺点是收敛速度较慢。文章中根据共轭梯度算法的收敛特性,在设计自适应滤波器时,对求取滤波器的均方误差函数最小值的计算过程进行优化设计,并通过计算机仿真验证了该算法可以使均方误差函数以较快的速度收敛到极小值点,从而得到滤波器权系数的最佳状态。     

集成多种自适应滤波算法的回声消除器

如何选择自适应算法的步长,从而有效解决收敛速度和稳态失调之间的矛盾是回声消除中的一个重要问题。论文提出一种集成多种自适应滤波算法的回声消除框架,以挖掘不同自适应滤波算法以及不同步长选择之间的互补性,来获得稳定的消除效果。所提算法可以分析同一时刻不同算法的误差,并始终选择一种最好的算法。通过对LMS、NLMS、PNLMS和IPNLMS这四种自适应算法的结合实验,显示了该算法可以集合各种算法以及步长选择的优点,具有更快的收敛速度和良好的稳态特性。     

一种新的可变步长LMS自适应滤波算法

在简单讨论基本LMS，变步长NLMS和LMS／F组合自适应滤波算法的基础上提出一种新的可变步长LMS自适应滤波算法，新算法引入修正系数ρ和遗忘因子λi=exp(-i)，并利用ρ和λi来产生新的步长参与迭代，计算机仿真结果表明，与基本LMS算法或变步长NLMS、LMS／F组合算法相比，新算法在保持算法简单这一特点的同时进一步加快了收敛速度，并能够收敛到更小且稳定的均方误差(MSE)。     

A general class of nonlinear normalized adaptive filteringalgorithms

The normalized least mean square (NLMS) algorithm is an important variant of the classical LMS algorithm for adaptive linear filtering. It possesses many advantages over the LMS algorithm, including having a faster convergence and providing for an automatic time-varying choice of the LMS stepsize parameter that affects the stability, steady-state mean square error (MSE), and convergence speed of the algorithm. An auxiliary fixed step-size that is often introduced in the NLMS algorithm has the advantage that its stability region (step-size range for algorithm stability) is independent of the signal statistics. In this paper, we generalize the NLMS algorithm by deriving a class of nonlinear normalized LMS-type (NLMS-type) algorithms that are applicable to a wide variety of nonlinear filter structures. We obtain a general nonlinear NLMS-type algorithm by choosing an optimal time-varying step-size that minimizes the next-step MSE at each iteration of the general nonlinear LMS-type algorithm. As in the linear case, we introduce a dimensionless auxiliary step-size whose stability range is independent of the signal statistics. The stability region could therefore be determined empirically for any given nonlinear filter type. We present computer simulations of these algorithms for two specific nonlinear filter structures: Volterra filters and the previously proposed class of Myriad filters. These simulations indicate that the NLMS-type algorithms, in general, converge faster than their LMS-type counterparts     

On the convergence behavior of the LMS and the normalized LMSalgorithms

It is shown that the normalized least mean square (NLMS) algorithm is a potentially faster converging algorithm compared to the LMS algorithm where the design of the adaptive filter is based on the usually quite limited knowledge of its input signal statistics. A very simple model for the input signal vectors that greatly simplifies analysis of the convergence behavior of the LMS and NLMS algorithms is proposed. Using this model, answers can be obtained to questions for which no answers are currently available using other (perhaps more realistic) models. Examples are given to illustrate that even quantitatively, the answers obtained can be good approximations. It is emphasized that the convergence of the NLMS algorithm can be speeded up significantly by employing a time-varying step size. The optimal step-size sequence can be specified a priori for the case of a white input signal with arbitrary distribution     

基于自适应系统辨识的收发隔离技术研究

收发隔离是机载干扰机不可避免的难题。如果收发隔离问题解决不好,轻则削弱干扰机效率,重则造成自发自收,形成自激励。固定步长的归一化最小均方误差(NLMS)算法在解决基于自适应系统辨识的收发隔离的问题时,由于精度不够,隔离效果很不理想。针对此问题提出一种基于先验误差的变步长NLMS算法,该算法依据相邻时刻先验误差的相关系数改变步长因子,改变后的步长因子能够在算法收敛过程中削弱噪声的影响,提高算法精度。理论分析和仿真结果证明:基于文中的变步长NLMS算法的收发隔离方案与基于其他最小均方误差算法的隔离方案相比,隔离性能有较大的改善。     

A bias‐compensated proportionate NLMS algorithm with noisy input signals

This paper proposes a novel proportionate normalized least‐mean‐squares (PNLMS) algorithm that is robust to input noises. Through compensating for biases due to input noise added at the filter input, the proposed PNLMS algorithm avoids performance deterioration owing to the noisy input signals. Moreover, since the proposed PNLMS algorithm uses a new gain‐distribution matrix, it has a fast convergence rate compared with the existing PNLMS algorithms, even when there is no input noise. The experimental results verify that the proposed PNLMS algorithm enhances the filter performance for sparse system identification in the presence of input noises.     

一种改进的数字同频直放站自适应反馈干扰抑制算法

基于数字地面电视广播(Digital Terrestrial Television Broadcasting,DTTB)同频直放站的回波干扰抑制,提出了一种变步长块LMS(Variable Step- size Block Normalized Least Mean Square,VSSBNLMS)自适应算法.此算法的目的是为了提高传统回波干扰抑制的自适应算法的收敛速度和降低计算复杂度.其将输入信号分为长度相等的块,在每一个数据块内,权值向量只更新一次,有效地降低了计算复杂度.另外,该算法通过输出误差控制更新步长的变化,与传统的归一化LMS(NLMS)和块LMS(BLMS)算法相比,提高了收敛速度.仿真结果表明,该算法具有良好的收敛速度和回波干扰抑制性能.     

New Variable Step-Sizes Minimizing Mean-Square Deviation for the LMS-Type Algorithms

The least-mean-square-type (LMS-type) algorithms are known as simple and effective adaptation algorithms. However, the LMS-type algorithms have a trade-off between the convergence rate and steady-state performance. In this paper, we investigate a new variable step-size approach to achieve fast convergence rate and low steady-state misadjustment. By approximating the optimal step-size that minimizes the mean-square deviation, we derive variable step-sizes for both the time-domain normalized LMS (NLMS) algorithm and the transform-domain LMS (TDLMS) algorithm. The proposed variable step-sizes are simple quotient forms of the filtered versions of the quadratic error and very effective for the NLMS and TDLMS algorithms. The computer simulations are demonstrated in the framework of adaptive system modeling. Superior performance is obtained compared to the existing popular variable step-size approaches of the NLMS and TDLMS algorithms.     

依据迭代系数状态因子分段的变步长NLMS算法

针对固定步长的归一化LMS算法（NLMS）存在不能同时兼顾收敛速度与稳态误差的问题,本文提出一种依据迭代系数状态因子进行分段的变步长NLMS算法。该变步长NLMS算法采用迭代系数状态因子作为表征迭代系数与实际系数的逼近状态的指标。当迭代系数状态因子值大于1,则说明迭代系数有偏离真实系数的趋势,此时采用步长因子较大的变步长方案;反之,说明迭代系数有逼近真实系数的趋势,应该采样步长因子较小的变步长方案。这样的自适应选择措施使得算法具有较强的收敛能力。理论分析和实验表明:在同样实验条件下,本文算法能够获得比其他文献更快的收敛速度和更小的稳态误差。      

Normalized LMS Adaptive Cancellation of Self-Image in Direct-Conversion Receivers

This paper presents an adaptive digital signal processing technique that cancels self-image interference due to frequency-independent, in-phase/quadrature-phase (I/Q) mismatch in zero-intermediate frequency (IF) direct-conversion receivers. The proposed technique, which is referred to as the normalized least-mean square adaptive self-image cancellation (NLMS-ASIC) algorithm, is an ASIC technique that controls the filter weight to minimize the power of the filter output signal using an NLMS type of weight-control mechanism. Some closed-form equations are derived for the mean-squared error (MSE), as well as the mean image-rejection ratio (IRR) of the proposed NLMS-ASIC algorithm. In particular, a step-size determination method is explained so that the requirements on the image-rejection performance and convergence time can be satisfied. The advantages of the proposed technique are demonstrated through computer simulations.