自适应噪声对消的归一化LMS算法

为了克服自适应滤波中固定步长LMS算法存在收敛速度与稳态误差的矛盾,本文通过MATLAB仿真不同步长因子下LMS算法的学习曲线,分析了LMS算法在收敛过程中存在的矛盾,并运用归一化LMS(NLMS)算法来改善上述矛盾。NLMS算法是通过输入变量改变步长因子从而改变算法的收敛特性。本文对NLMS与LMS算法的误差曲线仿真并进行稳态误差效果比较,结果显示NLMS算法的稳态误差精确度明显提高,收敛速度加快。通过将LMS算法与NLMS算法应用于自适应噪声对消中,得到NLMS算法具有收敛速度更快同时稳态误差更小的特性,该算法能够快速对干扰信号作出反应,使除噪效果更好。     

一种改进的变步长ELMS算法

在简单讨论基本最小均方(LMS)算法的基础上,引入了扩展的最小均方(ELMS)算法,并分析说明了该算法能达到更小的稳态MSE。改进的变步长ELMS算法是在对有用信号的预测中采用了自适应为归一化的的最小均方(NLMS)预测估计器,步长的迭代中引入遗忘因子i,利用其与误差信号的加权和来产生新的步长参与迭代。理论分析与计算机仿真结果表明,该算法有较好的收敛性能和较小的稳态失调。     

一种约束稳定性最小均方噪声对消算法

研究了最小均方误差(LMS)算法、归一化的最小均方(NLMS)算法及变步长NLMS算法在自适应噪声干扰抵消器中的应用,针对目前这些算法在噪声对消器应用中的缺点,将约束稳定性最小均方(CS-LMS)算法应用到噪声处理中,并进一步结合变步长的思想提出来一种新的变步长CS-LMS算法。通过MATLAB进行仿真分析,结果证实提出的算法与其他算法相比,能很好地滤除掉噪声从而得到期望信号,明显的降低了稳态误差,并拥有好的收敛速度。     

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)。     

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)。     

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.     

一种基于改进NLMS的多雷达主瓣干扰抑制方法

远程探测场景下弹载主瓣干扰因与目标角度间隔小，单站抗干扰方法性能下降严重。多雷达通过合理布站可有效“分辨”目标和干扰，被认为是可有效抑制主瓣干扰的途径。针对当前多雷达抗主瓣干扰方法普遍存在收敛速度慢的问题，提出一种基于改进归一化最小均方（NLMS）的多雷达对消抗主瓣干扰方法，并通过理论推导给出了其应用条件。该方法通过定义消散因子实现对步长的自适应控制从而达到快速收敛的目的，仿真结果表明该方法在收敛速度及收敛后均方误差（MSE）上具有明显优势。最后通过外场试验进一步验证该方法的性能，在满足布站条件且干噪比≥30 dB的前提下，信噪比改善可达19 dB以上，具有较高的应用价值。     

基于强散射点剔除的自适应窄带RFI抑制滤波器

在低频超宽带合成孔径雷达中,VHF/UHF频段密集的窄带射频干扰(RFI)严重影响了雷达性能。常规RFI抑制滤波器在干扰频点的陷波造成了宽带信号的能量损失,抬高了点目标的距离向旁瓣。该文提出一种可减小自适应滤波器旁瓣效应的方法:通过在距离压缩域剔除场景内的强散射点,减小输入信号中的宽带目标信号能量,提高自适应谱线增强器(ALE)对窄带干扰估计的精度,再从原始信号中减去干扰即得到目标回波信号。这种剔除强散射点的方法利用了匹配滤波后宽带信号与窄带干扰的时域特性差异,能有效降低自适应滤波器的旁瓣效应。该文选择归一化最小均方误差(NLMS)算法对剔除强散射点的自适应窄带RFI抑制滤波器进行了性能评估,与传统算法的对比试验表明该方法可在抑制RFI的同时有效减小强目标的距离向旁瓣。     

LMS自适应波束形成方法研究

研究了最小均方误差（LMS）和归一化最小均方误差（NLMS）自适应波束形成方法的性能,分析了影响波束形成性能的因素,通过计算机仿真实验验证了搜索步长、迭代次数、快拍对波束形成性能的影响,并比较了两种方法的收敛速度、稳态误差和抗干扰性能。     

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.     

Convergence and performance analysis of the normalized LMSalgorithm with uncorrelated Gaussian data

It is demonstrated that the normalized least mean square (NLMS) algorithm can be viewed as a modification of the widely used LMS algorithm. The NLMS is shown to have an important advantage over the LMS, which is that its convergence is independent of environmental changes. In addition, the authors present a comprehensive study of the first and second-order behavior in the NLMS algorithm. They show that the NLMS algorithm exhibits significant improvement over the LMS algorithm in convergence rate, while its steady-state performance is considerably worse     

外辐射源雷达抗直达波干扰技术研究

外辐射源雷达系统中,直达波干扰严重影响了雷达对目标的探测性能.文中针对直达波干扰问题,通过对LMS、NLMS、改进的NLMS算法的收敛速度、时变系统跟踪能力、失调量等的分析,将改进的归一化LMS(NLMS)自适应滤波算法应用于直达波干扰抑制,取得了较好的处理效果,其对消得益可达40 dB;分析了滤波器阶数、参数选择对对消性能和信噪比损失的影响,给出了典型参数值.最后,真实数据的处理结果验证了该方法的有效性.     

A Normalized Least Mean Square Algorithm Based on the Arctangent Cost Function Robust Against Impulsive Interference

In this paper, a normalized least mean square (NLMS) adaptive filtering algorithm based on the arctangent cost function that improves the robustness against impulsive interference is proposed. Owing to the excellent characteristics of the arctangent cost function, the adaptive update of the weight vector stops automatically in the presence of impulsive interference. Thus, this eliminates the likelihood of updating the weight vector based on wrong information resulting from the impulsive interference. When the priori error is small, the NLMS algorithm based on the arctangent cost function operates as the conventional NLMS algorithm. Simulation results show that the proposed algorithm can achieve better performance than the traditional NLMS algorithm, the normalized least logarithmic absolute difference algorithm and the normalized sign algorithm in system identification experiments that include impulsive interference and abrupt changes.     

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。      

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 set of algorithms linking NLMS and block RLS algorithms

This paper describes a set of block processing algorithms which contains as extremal cases the normalized least mean squares (NLMS) and the block recursive least squares (BRLS) algorithms. All these algorithms use small block lengths, thus allowing easy implementation and small input-output delay. It is shown that these algorithms require a lower number of arithmetic operations than the classical least mean squares (LMS) algorithm, while converging much faster. A precise evaluation of the arithmetic complexity is provided, and the adaptive behavior of the algorithm is analyzed. Simulations illustrate that the tracking characteristics of the new algorithm are also improved compared to those of the NLMS algorithm. The conclusions of the theoretical analysis are checked by simulations, illustrating that, even in the case where noise is added to the reference signal, the proposed algorithm allows altogether a faster convergence and a lower residual error than the NLMS algorithm. Finally, a sample-by-sample version of this algorithm is outlined, which is the link between the NLMS and recursive least squares (RLS) algorithms     

Adaptive sparse system identification using normalized least mean fourth algorithm

Normalized least mean square (NLMS) was considered as one of the classical adaptive system identification algorithms. Because most of systems are often modeled as sparse, sparse NLMS algorithm was also applied to improve identification performance by taking the advantage of system sparsity. However, identification performances of NLMS type algorithms cannot achieve high‐identification performance, especially in low signal‐to‐noise ratio regime. It was well known that least mean fourth (LMF) can achieve high‐identification performance by utilizing fourth‐order identification error updating rather than second‐order. However, the main drawback of LMF is its instability and it cannot be applied to adaptive sparse system identifications. In this paper, we propose a stable sparse normalized LMF algorithm to exploit the sparse structure information to improve identification performance. Its stability is shown to be equivalent to sparse NLMS type algorithm. Simulation results show that the proposed normalized LMF algorithm can achieve better identification performance than sparse NLMS one. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust Regularization for Normalized LMS Algorithms

We present a novel normalized least mean square (NLMS) algorithm with robust regularization. The proposed algorithm dynamically updates the regularization parameter that is fixed in the conventional$epsilon $-NLMS algorithms. By exploiting the gradient descent direction we derive a computationally efficient and robust update scheme for the regularization parameter. Through experiments we demonstrate that the proposed algorithm outperforms conventional NLMS algorithms in terms of the convergence rate and the misadjustment error.     

一种变步长自适应算法在GPS空时抗干扰中的应用

GPS空时二维联合抗干扰处理算法能将强干扰以及多径干扰的强度抑制到接近噪底,并且不会对原信号产生严重的损失或者扭曲。研究发现它的干扰抑制性能优于纯空域滤波。但是此算法比纯空域算法的复杂度高,因为需要处理的采样数据协方差矩阵的维数很大,很难做到实时处理。为了解决这一问题,目前工程中普遍采用的是最小均方误差算法(LMS)。通过分析功率倒置(PI)算法,给出了一种变步长的最小均方误差算法(NLMS)。此算法的干扰抑制性能优于最小均方误差算法,能达到多级维纳滤波算法(MSWF)的抑制效果,满足工程需要,并且运算量低,运算时间短,具有更好的可行性和实用性。并将此算法与LMS和MSWF算法进行了仿真对比,验证了其有效性。     

Effective Prediction for Video Traffic in Joint WiMAX/Satellite Networks

Combining WiMAX with satellite networks can be advantageous, especially in rural areas or locations affected by environmental factors. However, a satellite network experiences large round trip delays that may deteriorate quality especially for real-time applications. This paper improves the video prediction mechanism used for prediction of the uplink real-time traffic of an integrated satellite and WiMAX network. After a bibliographic search on mechanisms for video prediction in WiMAX and satellite networks, the NLMS (normalized least mean square) algorithm is chosen to be used as part of the existing mechanism, studying three possible alternatives. The first one proposes the implementation of the NLMS algorithm in the WiMAX BS (base station), the second one proposes the implementation of the NLMS algorithm in the satellite terminal, while the third one proposes the implementation of the NLMS algorithm in both the WiMAX BS and the satellite terminal. Simulation results show improved performance of all alternatives, while the best results are given by the second one which also has the lowest complexity in computations and memory.