Algorithm for multichannel LMS adaptive filtering

A modification of the LMS algorithm is presented in which the coefficients of an array of FIR filters, whose outputs are linearly coupled to an array of sensors, are adapted to minimise the mean-square sum of the error signals from these sensors. The application of the algorithm to the control of distributed parameter systems is discussed.     

Two-dimensional block diagonal LMS adaptive filtering

The paper is concerned with the development of two-dimensional (2D) adaptive filters using the block diagonal least mean squared (BDLMS) method. In this adaptive filtering scheme, the image is scanned and processed block by block in a diagonal fashion, and the filter weights are adjusted once per block rather than once per pixel. The diagonal scanning is adopted to avoid the problems inherent in the 1D standard scanning schemes and to account for the correlations in two directions. The weight updating equation for 2D BDLMS is derived, and the convergence properties of the algorithms are investigated. Simulation results that indicate the effectiveness of the 2D BDLMS when used for image enhancement, estimation, and detection applications are presented     

基于LMS算法的自适应滤波及在回声消除中的应用

介绍NLMS和NVLMS两种算法控制步长的思想。在此基础上，提出了新的变步长算法，同时使用误差积累和误差控制步长变化，并构建了基于自适应滤波算法的回声消除系统，将三种算法分别在此系统中应用，仿真验证了提出的算法具有更快的收敛速度和更小的稳态误差。并且在发生系统跳变时也能快速收敛。     

Constrained LMS adaptive algorithm

A new least-mean-squares (LMS) adaptive algorithm is developed in the letter. This new algorithm solves a specific variance problem that occurs in LMS algorithms in the presence of high noise levels and when the input signal is bandlimited. Quantitative results in terms of an accuracy measure of a finite impulse response (FIR) system identification are presented.     

Optimal variable step size for the LMS/Newton algorithm withapplication to subband adaptive filtering

An efficient approach for the computation of the optimum convergence factor for the LMS (least mean square)/Newton algorithm applied to a transversal FIR structure is proposed. The approach leads to a variable step size algorithm that results in a dramatic reduction in convergence time. The algorithm is evaluated in system identification applications where two alternative implementations of the adaptive filter are considered: the conventional transversal FIR realization and adaptive filtering in subbands     

Improved LMS algorithm for adaptive beamforming

Two adaptive algorithms which make use of all the available samples to estimate the required gradient are proposed and studied. The first algorithm is referred to as the recursive LMS (least mean squares) and is applicable for a general array. The second algorithm is referred to as the improved LMS algorithm and exploits the Toeplitz structure of the array correlation matrix and can be used only for an equispaced linear array     

语音去噪LMS自适应滤波器算法的改进

对LMS自适应算法进行了详细的性能分析与讨论,针对LMS算法运算较复杂、适应性较弱、稳定性差的缺点提出了一种HLMS(混合LMS)算法.建立了自适应噪声抵消系统,利用MATILAB软件对食堂、体育馆两处的录音信号进行计算机语音去噪仿真分析.实验结果表明,两种自适应方法均能有效抑制各种噪声污染,提高语音信噪比为60%～8...     

新变步长自适应算法及其时延估计性能

针对已有的变步长自适应算法收敛速度和稳态误差矛盾的问题,提出了一种新的变步长最小均方自适应滤波算法。新的算法在类S函数的基础上,引入调节因子P对步长函数的形状进行实时调整,并以误差的自相关时间均值估计调节步长,使得算法在初始时具有较快的收敛速度,稳态时有更平滑的步长变化。在新算法中引用最大似然加权算法进一步抑制自适应滤波器权系数伪峰。将新算法和最大似然加权应用在自适应时延估计的实验中,结果表明:在已有参数固定的条件下,新提出的算法具有更快的收敛速度和更小的稳态误差。同时,时延估计实验中能有效地实现信噪比-3 dB以上的准确时延估计。     

Complex fuzzy adaptive filter with LMS algorithm

A fuzzy adaptive filter is constructed from a set of fuzzy IF-THEN rules that change adaptively to minimize some criterion function as new information becomes available. This paper generalizes the fuzzy adaptive filter based on least mean squares (LMS) to include complex parameters and complex signals. The fuzzy filter as adaptive equalizer is applied to quadrature amplitude modulation (QAM) digital communication with linear complex channel characteristics     

An incomplete factorization algorithm for adaptive filtering

Many applications in signal processing lead to the solution of consecutive linear systems with very similar matrices. Here we present an efficient algorithm for solving this basic problem and compare it to the well-known Cholesky factorization.     

Convergence analysis of the sign algorithm for adaptive filtering

We consider the convergence analysis of the sign algorithm for adaptive filtering when the input processes are uncorrelated and Gaussian and a fixed step size μ>0 is used. Exact recursive equations for the covariance matrix of the deviation error are established for any step size μ>0. Asymptotic time-averaged convergence for the mean-absolute deviation error, mean-square deviation error, and for the signal mean-square estimation error are established. These results are shown to hold for arbitrary step size μ>0     

A parallel binary structured LMS algorithm for transversal adaptive filters

The adaptation process in digital filters requires extensive calculation. This computation makes adaptation a slow and time consuming process. Simple, but versatile, parallel algorithms for adaptive filters, suitable for VLSI implementation, are in demand. In this paper a regular and modular parallel algorithm for an adaptive filter is presented. This parallel structure is based on the Gradient Vector Estimation Algorithm, which minimizes the Mean Square Error. In the parallel method, the tap weights of the adaptive filter are updated everys steps, whereas in the recursive algorithms, the tap weights are updated at each step. Fors step update, bit strings of lengths are used to derive the terms with which the tap weights of the adaptive filter are calculated. The algorithm presented computes the tap weights at timen+s as a function of the tap weights at timen, the inputs from timen+1 ton+s−1, and the desired output from timen+1 ton+s−1. The algorithm also can be mapped to a VLSI architecture that is both regular and modular and allows either expansion of the order of the filter or the degree of parallelism obtainable. A comparison between the performance of the sequential LMS algorithm, Fast Exact LMS algorithm, and the parallel binary structured LMS algorithm is presented.     

A direction set based algorithm for adaptive filtering

A new algorithm is developed for adaptive filtering applications. This algorithm is based on a direction set method and has a computational complexity of O(N) for each update of the system. The method exploits the structure of the objective function and maintains a set of near-conjugate directions with respect to the Hessian. This algorithm has a rapid rate of convergence that is comparable with that of the well-known RLS method. The performance of the algorithm is illustrated with adaptive filtering applications     

Transform domain conjugate gradient algorithm for adaptive filtering

This paper proposed a new normalized transform domain conjugate gradient algorithm (NT-CGA), which applies the data independent normalized orthogonal transform technique to approximately whiten the input signal and utilises the modified conjugate gradient method to perform sample-by-sample updating of the filter weights more efficiently. Simulation results illustrated that the proposed algorithm has the ability to provide a fast convergence speed and lower steady-error compared to that of traditional least mean square algorithm (LMSA), normalized transform domain least mean square algorithm (NT- LMSA), Quasi-Newton least mean square algorithm (Q-LMSA) and time domain conjugate gradient algorithm (TD-CGA) when the input signal is heavily coloured.     

An adaptive nonlinear diffusion algorithm for filtering medicalimages

The nonlinear anisotropic diffusive process has shown the good property of eliminating noise while preserving the accuracy of edges and has been widely used in image processing. However, filtering depends on the threshold of the diffusion process, i.e., the cut-off contrast of edges. The threshold varies from image to image and even from region to region within an image. The problem compounds with intensity distortion and contrast variation. We have developed an adaptive diffusion scheme by applying the central limit theorem to selecting the threshold. Gaussian distribution and Rayleigh distribution are used to estimate the distributions of visual objects in images. Regression under such distributions separates the distribution of the major object from other visual objects in a single-peak histogram. The separation helps to automatically determine the threshold. A fast algorithm is derived for the regression process. The method has been successfully used in filtering various medical images     

Convergence of the sign algorithm for adaptive filtering withcorrelated data

Convergence of a decreasing gain sign algorithm (SA) for adaptive filtering is analyzed. The presence of the hard limiter in the algorithm makes a rigorous analysis difficult. Therefore, there are few results available. Such results normally include restrictive assumptions such as the assumptions that successive observation vectors are independent and the new error signal of the adaptive filter has a time invariant probability density function. The former assumption is not valid in the context of adaptive filtering since two successive observation vectors share most of their components, while the latter assumption is a restriction on the adaptive weights whose evolution is a priori unknown. In lieu of using these assumptions, an almost-sure convergence of the SA is proved under the assumption that the sequence of observation vectors is M-dependent. This assumption allows strong correlation between successive observations     

Wiener filtering and LMS algorithm of periodic nonuniformly sampledstochastic sequence

A study of Wiener filtering and an LMS algorithm of a periodic nonuniformly sampled stationary stochastic sequence is made. It is demonstrated that the Wiener-Hopf equation of such signals should be constructed and solved on each of the M staggered ordinals, a concept of periodic time-variant weights. Also shown is that in the LMS algorithm, M weight vectors should be adjusted respectively according to the staggered ordinals. Some conclusions and simulation results are given     

A fast quasi-Newton adaptive filtering algorithm

The convergence rate of an adaptive system is closely related to its ability to track a time-varying optimum. Basic adaptive filtering algorithms give poor convergence performance when the input to the adaptive system is colored. More sophisticated algorithms which converge very rapidly regardless of the input spectrum algorithms typically require O(N²) computation, where N is the order of the adaptive filter, a significant disadvantage for real-time applications. Also, many of these algorithms behave poorly in finite-precision implementation. An adaptive filtering algorithm is introduced which employs a quasi-Newton approach to give rapid convergence even with colored inputs. The algorithm achieves an overall computational requirement of O(N) and appears to be quite robust in finite-precision implementations     

改进的LMS自适应噪声对消算法的应用

针对工业噪声的特点,将自适应对消算法应用到工业噪声的处理中。根据传统最小均方(Least Mean Square,LMS)自适应算法的缺点,文中通过构造合适的步长因子,引入参数使得算法在提高收敛速度的同时保证较小的稳态误差。放宽算法的约束性条件,以提高步长调整的精度。实验验证,提出的算法与其他算法相比,具有更快的收敛速度、更小的稳态误差以及优良的抗干扰性能。