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。     

Fast adaptive algorithms for multichannel filtering and systemidentification

Fast transversal and lattice least squares algorithms for adaptive multichannel filtering and system identification are developed. Models with different orders for input and output channels are allowed. Four topics are considered: multichannel FIR filtering, rational IIR filtering, ARX multichannel system identification, and general linear system identification possessing a certain shift invariance structure. The resulting algorithms can be viewed as fast realizations of the recursive prediction error algorithm. Computational complexity is then reduced by an order of magnitude as compared to standard recursive least squares and stochastic Gauss-Newton methods. The proposed transversal and lattice algorithms rely on suitable order step-up-step-down updating procedures for the computation of the Kalman gain. Stabilizing feedback for the control of numerical errors together with long run simulations are included     

The p-norm generalization of the LMS algorithm for adaptive filtering

Recently much work has been done analyzing online machine learning algorithms in a worst case setting, where no probabilistic assumptions are made about the data. This is analogous to the H/sup /spl infin// setting used in adaptive linear filtering. Bregman divergences have become a standard tool for analyzing online machine learning algorithms. Using these divergences, we motivate a generalization of the least mean squared (LMS) algorithm. The loss bounds for these so-called p-norm algorithms involve other norms than the standard 2-norm. The bounds can be significantly better if a large proportion of the input variables are irrelevant, i.e., if the weight vector we are trying to learn is sparse. We also prove results for nonstationary targets. We only know how to apply kernel methods to the standard LMS algorithm (i.e., p=2). However, even in the general p-norm case, we can handle generalized linear models where the output of the system is a linear function combined with a nonlinear transfer function (e.g., the logistic sigmoid).     

Microstatistic LMS filtering

Adaptive microstatistic filters are developed for applications in which the second-order statistics of the thresholded signals are not known or may be nonstationary. A multilevel threshold decomposition such that real-valued stochastic processes can be filtered is used, and the computational complexity of the algorithm can be arbitrarily specified by the designer. The adaptation uses the least-mean-squares error approach of the least-mean-square (LMS) algorithm. The convergence of the adaptive algorithm is proved. Due to the nonhomogeneous statistical characteristic of the threshold signals, a different step-size adaptation parameter can be assigned to each threshold level. Simple design guidelines are developed for finding the set of nonhomogeneous step sizes which in practice yield better convergence characteristics     

Signal quality estimation with multichannel adaptive filtering in intensive care settings

A signal quality estimate of a physiological waveform can be an important initial step for automated processing of real-world data. This paper presents a new generic point-by-point signal quality index (SQI) based on adaptive multichannel prediction that does not rely on ad hoc morphological feature extraction from the target waveform. An application of this new SQI to photoplethysmograms (PPG), arterial blood pressure (ABP) measurements, and ECG showed that the SQI is monotonically related to signal-to-noise ratio (simulated by adding white Gaussian noise) and to subjective human quality assessment of 1361 multichannel waveform epochs. A receiver-operating-characteristic (ROC) curve analysis, with the human "bad" quality label as positive and the "good" quality label as negative, yielded areas under the ROC curve of 0.86 (PPG), 0.82 (ABP), and 0.68 (ECG).     

Cumulant-based adaptive multichannel filtering for wirelesscommunication systems with multipath RF propagation using antenna arrays

A method based on high-order statistics is proposed to mitigate the performance degradation caused by multipath RF propagation in a mobile radio communication system using a linear antenna array at the base-station receiver. It is shown that an overdetermined system of linear equations (involving only cumulants of the received baseband digitized signal) can be obtained to perform noniterative deconvolution. An efficient adaptive algorithm based on square-root decomposition is proposed to avoid numerical problems when real-time tracking of moving transmitters is needed     

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     

LMS coefficient filtering for Time-varying chirped signals

This paper presents coefficient filtering techniques in the least mean squares (LMS) algorithm to improve adaptive predictor tracking performance for time-varying chirped signals. The example application used in this paper is an electronic support measure (ESM) receiver for detecting radar chirped pulses. The leakage LMS, momentum LMS, and the proposed future-state coefficient (FC-LMS) filtering algorithms have been studied. The leakage LMS algorithm has the ability to remove the memory effect of the initial converged time-varying frequency of the chirped signal, thus improving the radar pulse detection performance. The momentum LMS is able to search for the time-varying optimum weight solution more efficiently, and the FC-LMS uses a parallel technique to retain the LMS throughput while being able to show a better tracking performance for chirped signals compared with the standard LMS algorithm.     

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     

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.     

Optimum filtering and multichannel receivers

We will examine the problem of optimum filtering when signals and noises are incoming on different channels. The filtering is defined by a rectangular matrix of filters and the non-linear operation in the signal processing is the general quadratic system. After the calculation of the output signal-to-noise ratio, we discuss the problem of optimum filtering. In this paper we consider only the solution in some particular cases as, for example, the correlator. In the second part, we discuss some properties of the optimum linear filters and we show in the third how the problem of directivity synthesis in omnidirectional noise is a particular case of optimum filtering.     

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。     

Blind multichannel identification using overlap-save filtering

An improved cross-relation (CR)-based blind multichannel estimator using the overlap-save filtering technique is proposed. The proposed method performs as well as the original CR method, but is implementationally more efficient.     

An adaptive recursive LMS filter

An adaptive, recursive, least mean-square-digital filter is heuristically derived that has the computational simplicity of existing transversal adaptive filters, with the additional capability of producing poles in the filter transfer function. Simulation results are presented to demonstrate its capability.     

Optimum variable step-size sequence for LMS adaptive filters

An optimum time-varying step-size sequence for adaptive filters employing the least mean squares algorithm is proposed. This step-size sequence is obtained after minimising the mean square error cost function with respect to the step size on an iterative, block-by-block basis. The effect of the various parameters on the optimum step-size sequence is described and a simple approximation of the optimum sequence is proposed. As compared with other optimum approaches, the proposed one gives a considerable reduction in convergence time and is implemented with just one extra multiplication. Computer simulations are performed to confirm the effectiveness of the new approach     

LMS算法的二次稳定性及鲁棒LMS算法

本文在时域内研究LMS算法(least mean square algorithm)的稳定性及鲁棒LMS算法的构造.首先将LMS算法表达式转化为标准的离散时间系统状态方程形式,之后运用线性矩阵不等式(LMI)技术对其二次稳定性进行了分析.针对滤波过程中会出现的输入和测量噪声干扰,本文提出了一种兼顾收敛性、鲁棒稳定性以及鲁棒性能的鲁棒LMS算法,最后给出了仿真算例,通过和一般的LMS算法的比较,体现了这种鲁棒LMS算法的优越性.     

Reduced-rank adaptive filtering

A novel rank reduction scheme is introduced for adaptive filtering problems. This rank reduction method uses a cross-spectral metric to select the optimal lower dimensional subspace for reduced-rank adaptive filtering as a function of the basis vectors of the full-rank space     

Nonlinear effects in LMS adaptive equalizers

An adaptive transversal equalizer based on the least-mean-square (LMS) algorithm, operating in an environment with a temporally correlated interference, can exhibit better steady-state mean-square-error (MSE) performance than the corresponding Wiener filter. This phenomenon is a result of the nonlinear nature of the LMS algorithm and is obscured by traditional analysis approaches that utilize the independence assumption (current filter weight vector assumed to be statistically independent of the current data vector). To analyze this equalizer problem, we use a transfer function approach to develop approximate analytical expressions of the LMS MSE for sinusoidal and autoregressive interference processes. We demonstrate that the degree to which LMS may outperform the corresponding Wiener filter is dependent on system parameters such as signal-to-noise ratio (SNR), signal-to-interference ratio (SIR), equalizer length, and the step-size parameter