New analytical model for the filtered-x least mean squares algorithm verified through active noise control experiment

This paper presents a new analytical model for the behavior of the Filtered-x LMS adaptive algorithm when applied to active noise control (ANC) in a finite duct. The new model is derived using a stochastic differential equation (SDE) approach. The theoretical predictions are verified through experimental results. The real-time system has been implemented using only I/O cards and a general purpose computer. Automation of the experimental setup allows the practical verification of the theoretical statistical model using Monte Carlo techniques. It is verified that the behavior of the algorithm is accurately predicted by the theoretical model.     

Gear fault diagnosis using active noise cancellation and adaptive wavelet transform

Acoustic signal from a gear mesh with faulty gears is in general non-stationary and noisy in nature. Present work demonstrates improvement of Signal to Noise Ratio (SNR) by using an active noise cancellation (ANC) method for removing the noise. The active noise cancellation technique is designed with the help of a Finite Impulse Response (FIR) based Least Mean Square (LMS) adaptive filter. The acoustic signal from the healthy gear mesh has been used as the reference signal in the adaptive filter. Inadequacy of the continuous wavelet transform to provide good time–frequency information to identify and localize the defect has been removed by processing the denoised signal using an adaptive wavelet technique. The adaptive wavelet is designed from the signal pattern and used as mother wavelet in the continuous wavelet transform (CWT). The CWT coefficients so generated are compared with the standard wavelet based scalograms and are shown to be apposite in analyzing the acoustic signal. A synthetic signal is simulated to conceptualize and evaluate the effectiveness of the proposed method. Synthetic signal analysis also offers vital clues about the suitability of the ANC as a denoising tool, where the error signal is the denoised signal. The experimental validation of the proposed method is presented using a customized gear drive test setup by introducing gears with seeded defects in one or more of their teeth. Measurement of the angles between two or more damaged teeth with a high level of accuracy is shown to be possible using the proposed algorithm. Experiments reveal that acoustic signal analysis can be used as a suitable contactless alternative for precise gear defect identification and gear health monitoring.     

主动噪声控制技术及其在车内噪声控制中的应用

主动噪声控制是集声学、自适应控制及数字信号处理等技术为一体的高新技术，已成为国内外噪声控制界的研究热点。基于主动噪声控制的Swinbanks多极子系统理论，证明了主动噪声控制技术适合于低频．1k的理论依据。阐述了决定主动噪声控制效果的决定因素及在车内噪声控制中应用的发展过程，并指出当前研究中需解决的问题和今后的研究方向。     

LYAPUNOV TUNING OF THE LEAKY LMS ALGORITHM FOR SINGLE-SOURCE,SINGLE-POINT NOISE CANCELLATION

Least-mean square (LMS) algorithms, which are commonly used for adaptive feedforward noise cancellation, have performance issues related to insufficient excitation, non-stationary reference inputs, finite-precision arithmetic, quantisation noise and measurement noise. Such factors cause weight drift and potential instability in the conventional LMS algorithm. Here, we analyse the stability and performance of the leaky LMS algorithm, which is widely used to correct weight drift. A Lyapunov tuning method is developed to find an adaptive leakage parameter and adaptive step size that provide optimum performance and retain stability in the presence of measurement noise on the reference input of known variance. The method accounts for non-persistent excitation conditions and non-stationary reference inputs and requires no a priori knowledge of the reference input signal characteristics other than a lower bound on its magnitude or a minimum signal-to-noise ratio. The Lyapunov tuning method is demonstrated for three candidate adaptive leakage and step size parameter combinations, each of which is a function of the instantaneous measured reference input, measurement noise variance, and/or filter length. These candidates illustrate stability vs performance tradeoffs in the leaky LMS algorithm elicited through the Lyapunov tuning method. The performance of each candidate Lyapunov tuned algorithm is evaluated experimentally in a single source, single-point acoustic noise cancellation system.     

基于TMS320C6713的去噪自适应滤波器硬件实现

我们每天都会受到音频噪音的影响,原始信息会受到周围环境噪声信号的污染。为此,本文研究了一个多抽头自适应去噪实时硬件系统,它利用TMS320C6713上实现的最小均方算法（LMs）来去除与音频相关的应用程序中接收到的不期望出现的噪声信号。文章首先介绍了最小均方算法的c语言实现并在CodeComposerStudio上进行仿真,最后在C6713上实现。考虑不同的音频输入,进行了三项实验来测试所设计的自适应去噪系统的效率。实验采用300、500、800、1000和3000Hz的音频信号及男性语音信号为输入的参考信号,持续检测信号中的噪声,直至将它全部去除。此外,还研究了与自适应去噪系统性能相关的收敛速度、滤波器安排顺序以及信噪比。实验结果表明,所设计系统其信噪比有很大的改善。     

次级声源安装角度对管道有源消声性能的影响研究

分别从理论和试验两个方面,对非适应和自适应两种管道有源消声系统中的次级声源安装角度对系统性能的影响进行了研究。结果表明,采用自适应结构具有明显的优越性。小的次级声源安装角度,可以优化有源消声系统的声波干涉条件,提高系统消声量,加快自适应有源消声系统收敛速度,并适当降低系统对次级声源的功率要求。对宽带有源消声系统而言,可以提高次级声源的指向性,减小系统存在的"声反馈"现象,提高控制系统的工作稳定性。最后,提出了几项实际工程设计原则。     

Adaptive methods for resolution enhancement of ultrasonic NDT signals: Comparative exploration

Adaptive filters, with their efficiency and simplicity, have been used successfully in various ultrasonic NDT signal processing contexts. Of these, the adaptive deconvolution with the conventional least-mean-squares (LMS) adaptive filter has improved time resolution. However; the convergence speed of LMS is restricted by the eigenvalue spread of the input correlation matrix. This paper explores the potential of other adaptive algorithms, namely, normalized least-mean-squares (NLMS), recursive least squares (RLS) and QR-decomposition-based RLS (QR-RLS) to handle the deconvolution of ultrasonic NDT signals and compare their performances with that of the conventional LMS algorithm. Furthermore, the mean square error (MSE) behavior in the different adaptive filtering algorithms for ultrasonic NDT signals deconvolution is briefly introduced. Experiments results are explained by graphs and discussed based on the performance criteria. The proposed methods enhanced the resolution quality, offering more alternatives for this application according to specific case requirements.     

LMS algorithm for active noise control with improved gradient estimate

Most existed LMS algorithms have noisy gradient estimate since they use instantaneous square error to estimate ensemble average of the square errors. In this paper, a new type of LMS algorithm is developed for active noise control. The gradient estimate in the proposed algorithm is improved by using the weighted time average instead of the instantaneous value to approximate the ensemble average. Compared to the filtered-x LMS algorithm, the proposed algorithm is shown to have many improved convergence properties. Computer simulations are given to support the theoretical analysis.     

Acceleration tracking control combining adaptive control and off-line compensators for six-degree-of-freedom electro-hydraulic shaking tables

An electro-hydraulic shaking table (EHST) is an essential experimental facility to simulate in real-time actual vibration situations. An adaptive controller combined with off-line compensators is proposed to improve the acceleration frequency bandwidth and tracking accuracy of a six-degree-of-freedom (6-DOF) EHST. A servo controller has been employed to implement acceleration closed-loop and coordinate control of the 6-DOF EHST. A recursive extended least-squares algorithm is employed to identify acceleration closed-loop transfer functions and a zero-phase-error tracking controller is used to design off-line inverse model compensators using the identified transfer functions. However, the off-line compensators cannot compensate in real-time varying dynamics of the 6-DOF EHST; so an online adaptive controller with a least-mean-squares (LMS) algorithm based on a delay compensator is employed. The proposed controller combines advantages of the off-line compensators and the online adaptive controller, which guarantees both a fast rate of convergence of the LMS algorithm and high-fidelity acceleration tracking accuracy of the 6-DOF EHST. Some experimental studies have been conducted on a 6-DOF EHST and experimental results show that acceleration tracking control performances, including the rate of convergence of the LMS algorithm and acceleration tracking accuracy, have been improved compared to a conventional three-variable controller and adaptive controllers.     

IMPROVEMENT ON STABILITY OF ADAPTIVE NOISE CONTROL SYSTEM BY USE OF GRADIENT BACK-ADJUSTING FLMS ALGORITHM

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基于多尺度小波变换的变步长LMS滤波算法

对LMS自适应算法、基于抽样函数的变步长LMS算法和基于多尺度小波变换的自适应滤波算法进行了研究,在此基础上把变步长LMS算法与多尺度小波变换相结合,产生了新算法。该算法一方面可以克服固定步长LMS算法在收敛速度与收敛精度方面与步长因子的矛盾;另一方面,小波变换的引入减少了输入向量自相关矩阵的条件数,提高了收敛速度、跟踪性能和稳定性。最后对算法的性能进行了计算机仿真比较,仿真结果表明:基于多尺度小波变换的变步长LMS滤波算法具有较快的收敛速度和更强的抑噪能力。     

一种稳健的窄带反馈型主动噪声控制系统

传统窄带反馈型主动噪声控制系统采用并型自适应滤波器结构,用于合成参考信号时,仍存在参考信号合成质量较差、 滤波器权值的初值设置难度高、应对非平稳窄带噪声的能力不足等问题。 为解决以上问题,通过引入高效的串并型自适应滤波 器结构来合成参考信号,提出新型的窄带反馈型主动噪声控制系统,改善参考信号合成质量,进而提升系统的整体降噪性能。 新型系统不仅有效解决了传统系统存在的参考信号合成质量较差的问题,还降低了滤波器权值的初值设置复杂度,同时还提升 了系统应对非平稳窄带噪声的能力。 实验结果表明,新型系统较传统系统的前后两部分的降噪量分别提升了 7. 89 和 9. 18 dB。 仿真和实验结果共同验证了提出的系统较传统系统具有更强的稳健性和降噪性能。     

管道复合主动噪声控制系统及其实现

为了提高管道噪声主动控制系统的性能,设计出基于TI公司的TMS320C30芯片作为控制器的实时管道复合噪声主动控制系统(简称ANC系统)。针对前馈ANC可实现较宽的降噪频带,但在实际复杂系统中不能完全满足参考传感器信号和噪声声源要紧密相关要求;而反馈ANC系统中,降噪效果不依赖于声场的相关性,但其降噪频带较窄的特点,设计出复合FXLMSANC系统。在某风机、通风管路系统上进行ANC试验的结果表明,应用复合FXLMSANC方法,能获得比单独使用前馈FXLMSANC算法或反馈FXLMSANC算法更好的降噪效果。     

RLS and LMS blind adaptive multi-user detection method and comparison in acoustic communication

RLS and LMS blind adaptive multi-user detection algorithm and multi-user detector was proposed to solve the problem of multi-user signal detection problem encountered in underwater acoustic communication networks.In simulation analysis,RLS and the LMS blind adaptive multi-user detector were designed and tested for synchronous and asynchronous multi-user communication process.The results of SIR comparison and MMSE comparison show that,both of the two methods can realize blind adaptive detection when any user change in multi-user communication,during this process,the training communication sequences are not needed.The RLS algorithm has about 5 dB higher in SIR compared with LMS algorithm,and the convergence velocity of RLS algorithm is also higher than LMS algorithm when the communication users change.RLS algorithm has better ability in multi-user detection than that of LMS algorithm,and it has great attraction and guiding significance for solving the problem of multiple access interference(MAI) in multi-user communication.     

A local active noise control system based on a virtual-microphone technique for railway sleeping vehicle applications

Low-frequency broadband noise generated on a railway vehicle by the wheel–rail interaction could be a big annoyance for passengers in sleeping cars. Low-frequency acoustic radiation is extremely difficult to attenuate by using passive devices. In this article, an active noise control (ANC) technique has been proposed for this purpose. A three-dimensional cabin was built in the laboratory to carry out the experiments. The proposed scheme is based on a Filtered-X Least Mean Square (FXLMS) control algorithm, particularised for a virtual-microphone technique. Control algorithms were designed with the Matlab-Simulink tool, and the Real Time Windows Target toolbox of Matlab was used to run in real time the ANC system. Referring to the results, different simulations and experimental performances were analysed to enlarge the silence zone around the passenger's ear zone and along the bed headboard. Attenuations of up to 20 and 15 dB(A) (re:20 μPa) were achieved at the passenger's ear in simulations and in experimental results, respectively.     

一种低复杂度快速收敛的窄带主动噪声控制系统

传统窄带主动噪声控制( ANC)系统的计算负荷与窄带噪声频带数量正相关,而且其收敛速度和降噪效果存在此消彼 长的关系。 为解决以上问题,首先基于滤波误差技术简化窄带 ANC 模型,使系统计算量与窄带噪声频带数量相互独立。 然后 通过自适应混合参数将两个简化的窄带 ANC 系统进行并行凸组合,其中一个系统拥有最快的收敛速度,另一个系统拥有较低 的稳态误差。 提出的系统不仅降低了传统窄带 ANC 系统的计算复杂度,而且在不牺牲降噪性能的情况下提升了系统收敛速 度。 实验结果表明,提出系统在不牺牲稳态误差的基础上,收敛时间缩短了 78% ,有效提升了系统收敛性能。     

一种MCG信号的自适应预测滤波方法

与心电圈相比较,心磁检测具有无需电极,对某些局部心肌电流高度敏感,可以用于早期心脏疾病的诊断。但心脏磁场信号在检测过程中会被噪声所污染,使得信号本身的可辨识性降低,因此需要对该信号进行降噪处理。在单通道信号采集系统无噪声参考输入端的情况下采用自适应滤波方法,需要对待处理信号进行线性预测,本文提出的改进LMS（Least Mean Square）算法的自适应预测滤波器,无需噪声参考信号即可对心磁信号进．彳亍滤波,通过三种不同噪声的滤波仿真结果可见,采用自适应预测滤波器处理后明显提高了信噪比,具有一定的学术意义和实用价值。     

QRS detection using adaptive filters: A comparative study

Electrocardiogram (ECG) is one of the most important physiological signals of human body, which contains important clinical information about the heart. Monitoring of ECG signal is done through QRS detection. In this paper, an improved QRS detection algorithm, based on adaptive filtering principle, has been designed. Enumeration of the effectiveness of various LMS variants used in adaptive filtering based QRS detection algorithm has been done through fidelity parameters like sensitivity and positive predictivity. Whole family of LMS algorithm has been implemented for comparison. Sign-sign LMS, sign error LMS, basic LMS and normalized LMS are re-implemented, while variable leaky LMS, variable step-size LMS, leaky LMS, recursive least squares (RLS), and fractional LMS are novel combination presented in this paper. After analysis of the obtained results, performance of leaky-LMS algorithm is found to be the best with sensitivity, positive predictivity, and processing time of 99.68%, 99.84%, and 0.45 s respectively. Reported results are tested and evaluated over MIT/BIH arrhythmia database. Presented study also concludes that the performance of most of the variants gets affected due to low SNR but the Leaky LMS performs better even under heavy noise conditions.     

Estimation of time-varying power quality indices using a computationally efficient algorithm

In this paper a new recursive adaptive filter based on a fast Gauss–Newton method has been proposed for the estimation of power quality (PQ) indices for time-varying voltage and current signals in an electric power system. The presented algorithm is based on the minimization of a weighted forgetting factor based error cost function by the use of Recursive Gauss–Newton method. Further a Hessian matrix approximation is used to produce a fast recursive algorithm, which is immune to random noise, waveform distortion and increases the speed of convergence and accuracy. The algorithm models the typical time-varying signal and the accompanied distortions due to harmonics and random noise in a manner that will be suitable for real-time PQ indices estimation. Further, the forgetting factor is tuned in accordance with signal error covariance to provide improved performance. Also power system frequency variations are estimated and correction factors are derived. The effects of sub harmonics, and interharmonics in the signal have been considered while estimating the various PQ indices.     

Adaptive frequency tuner for active narrowband noise control systems

The reference frequency error can significantly degrade the performance of the narrowband active noise control (ANC) system. This paper investigates the relationship between the reference frequency error and the performance of the narrowband ANC system, and presents an adaptive frequency tuner which can reduce the reference frequency error and improve the performance of the system. Effectiveness of the adaptive frequency tuner is validated by simulation results using experimental transfer functions.