基于小波变换的变步长LMS自适应滤波算法

变步长LMS自适应滤波算法通过构造合适的步长因子有效的解决了传统LMS算法收敛速度和稳态误差相矛盾的问题.变换域LMS自适应滤波算法通过正交变换降低了输入信号矩阵的相关性,提高了算法的收敛速度.将这两种算法相结合,提出了一种新的基于小波变换的变步长LMS自适应滤波算法.仿真结果表明,该算法无论是收敛速度还是稳态误差都有了很大的提高.     

Line search algorithms for adaptive filtering

Line search algorithms for adaptive filtering that choose the convergence parameter so that the updated filter vector minimizes the sum of squared errors on a linear manifold are described. A shift invariant property of the sample covariance matrix is exploited to produce an adaptive filter stochastic line search algorithm for exponentially weighted adaptive equalization requiring 3N+5 multiplications and divisions per iteration. This algorithm is found to have better numerical stability than fast transversal filter algorithms for an application requiring steady-state tracking capability similar to that of least-mean square (LMS) algorithms. The algorithm is shown to have faster initial convergence than the LMS algorithm and a well-known variable step size algorithm having similar computational complexity in an adaptive equalization experiment     

一种基于多尺度小波变换的自适应滤波新算法

将多尺度小波变换的理论引入到LMS自适应滤波器的设计中,分析了基于多尺度正交小波变换的自适应滤波算法的原理;将变步长LMS算法与多尺度小波变换的思想结合,提出了一种新的小波自适应滤波算法(MSWT-MVSS-LMS),新算法既减少了输入向量自相关矩阵条件数,又克服了固定步长LMS算法在收敛速度与收敛精度方面与步长因子μ的矛盾,获得了更好的收敛速度和稳定性.仿真结果表明新算法是有效的和优越的.     

基于小波变换变步长LMS自适应信道均衡的新方法

结合变换域最小均方(LMS)和变步长LMS算法的优势,提出了一种基于小波变换的变步长LMS自适应均衡方法。该方法中步长调整函数采用了改进的Sigmoid函数,该函数具有简单且误差信号接近零时变化缓慢的特点。并且,在训练模式、判决引导模式以及混合模式下,将提出方法和传统均衡方法进行了仿真比较。结果表明,所提出的方法比传统的线性LMS算法、变步长LMS以及小波变换LMS收敛更快、性能更优。     

DFT/LMS算法在DSSS中的应用及性能分析

本文分析了直接序列扩频(DSSS)系统中最小错误概率(MPE)意义下的最优滤波器,并依据矩阵求逆引理证明最小均方误差(MMSE)意义下的最优滤波——维纳滤波也是MPE意义下的最优滤波。在DSSS中应用自适应滤波,无须先验已知扩频码的码型和干扰的统计特性,就能一并完成解扩以及有效抑制干扰。离散傅立叶变换/最小均方(DFT/LMS)算法的收敛速度远快于LMS算法,而运算量、稳健性与LMS算法基本相同。基于DFT/LMS算法的自适应滤波大大简化DSSS系统接收机的设计,显著增强系统抗干扰能力,具有很强的实用性。     

基于正交小波变换的瞬变步长LMS自适应滤波算法

本文在滑动准最小均方误差瞬变步长LMS（SPLMS）算法基础上提一种基于正交小波变换的瞬变步长LMS（WTSPLMS）的改进自适应滤波算法,该算法较好地利用了正交小波具有良好的时频局部化的特性,使之除具有SPLMS算法的优点外,还具有更高的起始收剑速率,更小的权失调噪声,更大的抑噪能力,计算机仿真效果较好,支持了理论分析。     

基于图像增强的改进自适应加权均值滤波算法

为了尽可能滤除图像中的椒盐噪声同时改善图像视觉效果,将改进自适应加权均值滤波与小波域图像增强技术有机结合,提出了一种具有增强效果的图像滤波算法。该算法分为滤波和滤波后处理两个阶段。滤波阶段,对经典均值滤波分别从噪声检测策略、权值计算机方法噪声滤波模版设计等方面进行适当改进,给出了具体实现步骤;滤波后处理阶段,首先将滤波后图像进行三层小波分解;然后构造出一种小波图像增强模型,根据小波系数的幅度值将其分为三个部分,分别进行不同程度的拉伸处理;最后进行拉伸后小波系数重构。将该滤波算法与经典均值滤波,加权均值滤波、自适应加权中值滤波等性能比较,实验结果表明,本文滤波算法在噪声滤除和图像细节保持方面,效果较好。     

一种基于自适应滤波的语音降噪方法研究

分析和研究自适应滤波和小波变换法的原理及方法,提出了一种新的综合使用自适应滤波和小波变换法的语音降噪方法。该方法首先用仿生小波变换法对带噪声的语音信号进行小波分解,将小渡变换法分离出来的噪声信号作为自适应滤波器的输入。最后选择用最小均方误差（LMS）的自适应算法对带噪声语音信号进行降噪处理,实现了信噪分离,去除语音信号中的噪声信号。实验结果表明,该方法对语音信号有较为明显的降噪效果。     

基于变换域全相位FIR自适应滤波算法

基于一种全相位FIR自适应滤波器,将重叠滤波思想引入变换域LMS算法,提出了DFT、DCT和DST变换域的带窗重叠自适应滤波算法(WO-TLMS).与传统的变换域LMS(TLMS)算法相比,WO-TLMS算法提高了收敛速度同时具有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TLMS算法的比较验证了WO-TLMS算法的优越性.     

MIMO-OFDM系统中一种基于自适应滤波的信道估计方法

该文提出了一种适用于MIMO-OFDM系统的基于自适应滤波器的信道估计方法,此方法在不需要任何信道统计信息的前提下,通过自适应滤波的方法对时变信道状态参数进行即时跟踪与估计。仿真结果表明该文提出的基于自适应滤波的信道估计方法,相比于不考虑噪声的基于LS算法的信道估计方法,MSE和BER性能均有很大的提高。其中基于LMS滤波器的信道估计方法具有计算复杂度小的特点;而基于RLS的信道估计方法具有收敛速度快,MSE和BER性能均优于基于LMS方法的特点。     

LFM信号的分数阶傅里叶域自适应滤波算法研究

对于线性调频信号(LFM)的滤波,采用处理平稳信号的方法对其滤波往往得不到很好的效果。本文利用了线性调频信号在分数傅里叶变换域上具有很好的时频聚焦性的特点,来实现信号在分数阶傅里叶域的自适应滤波,自适应滤波算法采用改进的步长LMS方法,对传统的LMS算法做出了改进,算法中步长处理中引入了一个限制因子,可以较好地解决算法收敛速度和稳态失调量之间的矛盾。仿真结果表明,此算法在处理分数阶域的LFM信号滤波比传统的LMS算法有较好的滤波效果。      

Transform domain adaptive linear phase filter

Describes a new adaptive linear-phase filter whose weights are updated by the normalized least-mean-square (LMS) algorithm in the transform domain. This algorithm provides a faster convergence rate compared with the time domain linear phase LMS algorithm. Various real-valued orthogonal transforms are investigated such as the discrete cosine transform (DCT), discrete Hartley transform (DHT), and power of two (PO2) transform, etc. By using the symmetry property of the transform matrix, an efficient implementation structure is proposed. A system identification example is presented to demonstrate its performance     

自适应滤波器的修正的时域正交性算法及其应用

本文绘出了一个自适应滤波器的修正的时域正交性算法.计算机模拟结果表明,该算法有两个特性:一、相同的收敛因子c可以在比较宽范围的输入信号信噪比内使用。二、应用这种方法的噪声消除器的一个实用特性是,对于相同的期待信号,输入的参考信号的功率变化,不影响收敛因子c值的选择,这就使这种噪声消除器的应用更灵活。文中给出了这种算法用于谱估值时的特性,并与LMS算法进行了比较。     

离散小波变换域LMS自适应消噪

本文按照自成体系的离散小波变换正交分解框架,直接从离散域出发,得出离散小波变换域ＬＭＳ（ＤＷＬＭＳ）,变换的正交性与采样率无关,并可使用Ｍａｌｌａｔ快速算法。方法可减小自适应应滤波器输入向量自相关阵的谱动态范围,提出了传统ＬＭＳ算法的收敛速度和稳定性。     

A modified LMS algorithm for fast processing — analysis and realization

Fast convergence and simplicity of the real-time implementation of the adaptive filter algorithm are desirable in several applications. In this paper, a fast modified least mean square (LMS) algorithm is presented and analysed. The performance of the LMS and the modified LMS algorithm is compared with the help of both simulation and experimental results. Once the algorithms reach the track period (i.e. steady-state conditions), their performance is found to be essentially the same. The tracking performance of the modified algorithm is better as it operates twice as fast as the LMS algorithm.     

Adaptive filter for event-related bioelectric signals using animpulse correlated reference input: comparison with signal averagingtechniques

Many bioelectric signals result from the electrical response of physiological systems to an impulse that can be internal (ECG signals) or external (evoked potentials). In this paper an adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). We use the LMS algorithm to adjust the weights in the adaptive process. First, we show that the AICF is equivalent to exponentially weighted averaging (EWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials.     

Weighted Adaptive Lifting-Based Wavelet Transform for Image Coding

In this paper, a new weighted adaptive lifting (WAL)-based wavelet transform is presented. The proposed WAL approach is designed to solve the problems existing in the previous adaptive directional lifting (ADL) approach, such as mismatch between the predict and update steps, interpolation favoring only horizontal or vertical direction, and invariant interpolation filter coefficients for all images. The main contribution of the proposed approach consists of two parts: one is the improved weighted lifting, which maintains the consistency between the predict and update steps as far as possible and preserves the perfect reconstruction at the same time; another is the directional adaptive interpolation, which improves the orientation property of the interpolated image and adapts to statistical property of each image. Experimental results show that the proposed WAL-based wavelet transform for image coding outperforms the conventional lifting-based wavelet transform up to 3.06 dB in PSNR and significant improvement in subjective quality is also observed. Compared with the ADL-based wavelet transform, up to 1.22-dB improvement in PSNR is reported.     

Fast coupled adaptation for sparse impulse responses using a partial haar transform

This work presents a novel scheme for identifying the impulse response of a sparse channel. The scheme consists of two adaptive filters operating sequentially. The first adaptive filter adapts using a partial Haar transform of the input and yields an estimate of the location of the peak of the sparse impulse response. The second adaptive filter is then centered about this estimate. Both filters are short in comparison to the delay uncertainty of the unknown channel. The principle advantage of this scheme is that two short adaptive filters can be used instead of one long adaptive filter, resulting in faster overall convergence and reduced computational complexity and storage. The scheme is analyzed in detail for a least mean squares (LMS) LMS-LMS type of structure, although it can be implemented using any combination of adaptive algorithms. Monte Carlo simulations are shown to be in good agreement with the theoretical model for the behavior of the peak estimating filter as well as for the mean square error (MSE) behavior of the second filter.     

Adaptive filtering in subbands using a weighted criterion

Transform-domain adaptive algorithms have been proposed to reduce the eigenvalue spread of the matrix governing their convergence, thus improving the convergence rate. However, a classical problem arises from the conflicting requirements between algorithm improvement requiring rather long transforms and the need to keep the input/output delay as small as possible, thus imposing short transforms. This dilemma has been alleviated by the so-called “short-block transform domain algorithms” but is still apparent. This paper proposes an adaptive algorithm compatible with the use of rectangular orthogonal transforms (e.g., critically subsampled, lossless, perfect reconstruction filter banks), thus allowing better tradeoffs between algorithm improvement, arithmetic complexity, and input/output delay. The method proposed makes a direct connection between the minimization of a specific weighted least squares criterion and the convergence rate of the corresponding stochastic gradient algorithm. This method leads to improvements in the convergence rate compared with both LMS and classical frequency domain algorithms     

一种新的变步长变换域自适应滤波算法

变换域LMS算法能通过正交变换有效降低输入信号自相关矩阵特征值的分散程度,可提高算法的收敛速度;变步长LMS算法可以克服固定步长因子所导致的算法在较快收敛速度和较小稳态误差之间存在的矛盾,从而获得较快的收敛速度和较好的收敛结果。将二者相结合,提出了一种新的变步长变换域自适应滤波算法。计算机仿真结果表明该算法具有更快的收敛速度和更小的稳态误差,并且运算量较少,具有良好的实用性能。