Application of RLS adaptive filtering in signal de-noising

针对信号中混有噪声的问题,介绍了一种基于RLS算法的自适应信号消噪系统,并阐述了自适应滤波的原理以及RLS算法的步骤与流程。通过实例仿真,得到了基于RLS算法的自适应消噪系统仿真图。经过对比分析可知,RLS自适应滤波能较好地消除噪声,获得有用信号,从而验证了该方法的有效性和系统的合理性。     

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.     

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.     

The application of the neural network on Morse code recognition for users with physical impairments.

Morse code is a simple, speedy and low cost means of communication composed of a series of dots, dashes and space intervals. Each tone element (either a dot, dash or space interval) is transmitted by sending a signal for a defined length of time. This poses a challenge as the automatic recognition of Morse code is dependent upon maintaining a stable typing rate. In this paper, a suitable adaptive automatic recognition method, combining the least-mean-square (LMS) algorithm with a neural network, was applied to this problem. The method presented in this paper is divided into five modules: space recognition, tone recognition, learning process, adaptive processing and character recognition. Statistical analyses demonstrated that the proposed method elicited a better recognition rate in comparison with other methods in the literature.     

A comparative study of adaptation algorithms for nonlinear system identification based on second order Volterra and bilinear polynomial filters

Nonlinear filtering techniques have recently become very popular in the field of signal processing. In this study we have considered the modeling of nonlinear systems using adaptive nonlinear Volterra filters and bilinear polynomial filters. The performance evaluation of these nonlinear filter models for the problem of nonlinear system identification has been carried out for several random input excitations and for measurement noise corrupted output signals. The coefficients of the two candidate filter models for are designed using several well known adaptive algorithms, such as least mean squares (LMS), recursive least squares (RLS), least mean p-norm (LMP), normalized LMP (NLMP), least mean absolute deviation (LMAD) and normalized LMAD (NLMAD) algorithms. Detailed simulation studies have been carried out for comparative analysis of Volterra model and bilinear polynomial filter, using these candidate adaptation algorithms, for system identification tasks and the superior solutions are determined.     

一种新的LMS算法及其在明渠流量计中的应用

针对变步长LMS(least mean square)自适应滤波算法存在收敛速度慢和稳态误差比较大的问题,根据变步长滤波算法步长调整原理,提出了一种新的基于多项式的非线性变步长LMS算法,通过MATLAB仿真分析研究新算法中各参数对收敛速度和稳态误差的影响,并和已有的变步长LMS算法进行对比,结果表明该算法比已有的算法收敛速度快4倍,且稳态误差小。将新算法应用于明渠流量计回波信号的滤波处理,结果表明在低信噪比下,明渠流量计滤波性能良好,抗干扰能力强。     

电液振动台正弦振动自适应控制方法

电液振动台是大型结构正弦振动模拟实验的关键设备。受液压系统频宽及系统中存在的死区、摩擦力等因素影响,正弦振动实验中加速度输出信号存在跟踪精度低、波形失真度大等问题。为提高电液振动台控制精度,提出正弦振动自适应逆控制及谐波抑制复合控制策略。通过带遗忘因子的RLS算法构建自适应逆控制器,提高正弦波形的跟踪精度。基于快速块LMS算法构建双次自适应谐波抑制控制器,减小系统加速度输出信号波形失真度。最后通过振动台实验,验证了控制策略的有效性。     

压电复合材料结构模糊自适应前馈振动控制

针对压电复合材料结构前馈主动控制中存在的某些非线性问题 ,本文提出一种模糊自适应前馈主动控制方法 ,解决一类参考信号与外扰呈非线性函数关系的主动控制问题。数值仿真结果表明 ,该非线性模糊自适应控制方法优于线性滤波 - x L MS方法的控制效果     

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。     

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。     

电域自适应中红外激光甲烷传感系统

为了抑制甲烷传感器中统计特性无法预知的电学噪声,本文结合递归最小二乘自适应去噪算法和直接吸收光谱技术,使用中心波长为3 291nm的带间级联激光器和多反射吸收气室,研制了一种电域自适应中红外甲烷传感器系统。在传统探测器输出信号(称为信号通道)的基础上,增加了激光器电流驱动器的反馈信号作为噪声通道来感知电学噪声。利用MATLAB软件对最小二乘法在直接吸收光谱技术中的滤波效果进行了仿真。通过在激光器驱动信号中施加不同的噪声,实验验证了最小二乘法的去噪效果。针对该传感器的Allan标准差结果表明,当不使用自适应最小二乘法时,系统在积分时间为6s的检测下限为78.8nL/L;使用RLS自适应算法时,系统的检测下限为43.9nL/L。相比基于传统传感结构和滤波方法的中红外直接吸收光谱传感器,本文所报道的中红外甲烷传感器由于采用了电域自适应滤波方法,因而呈现出更好的抗干扰性和稳定性。     

周期振动隔离的自适应方法及其实验研究

针对多频、多点隔振问题,将跟踪滤波与带饱和抑制的LMS算法相结合,构造出基于分量控制的自适应隔振方法,将其用于多频率多通道控制,并进行实验验证。该方法是在通用的LMS算法的基础上,引入饱和抑制函数,根据控制饱和程度,优化控制器权系数更新过程,避免控制器的性能恶化。实验结果表明,自适应方法能够在多通道控制中很好地隔离周期振动,不仅具有良好的稳态控制效果,而且可以改善输出饱和情况下的控制性能。     

Design of Time-Domain Equalizer in Elastic Wave Earth Channel

In low-frequency elastic wave through-the-earth communication system,because of multipath transmission caused by characteristics of the layered earth,the time domain equalizer is different from other wireless communication systems.A modified LMS algorithm of variable step size is proposed based on improvement of traditional LMS.On the base of principle and simulation analysis,the improved Least Mean Square(LMS)algorithm is analyzed and the performances are compared between the improved LMS algorithm and traditional LMS algorithm.In the improved algorithm,the contradiction between convergence speed and the steady-state error is considered at the same time.Therefore,the improved algorithm has good convergence properties and channel-tracking performance.     

基于递推最小二乘法与模糊自适应扩展卡尔曼滤波相结合的车辆状态估计

针对汽车状态估计中模型参数的变化和观测噪声的时变特性,提出了递推最小二乘法与模糊自适应扩展卡尔曼滤波相结合的汽车状态估计算法。为实现模型参数与观测噪声的实时更新,建立了基于三自由度非线性车辆动力学模型的算法,首先利用递推最小二乘法对汽车的总质量进行估计,其次建立了模糊控制器对扩展卡尔曼滤波的观测噪声进行实时跟踪。在搭建的CarSim与MATLAB/Simulink联合仿真平台中验证了该算法的有效性,结果表明该算法估计精度高于传统扩展卡尔曼滤波算法,研究结果为汽车的主动安全控制提供了理论支持。     

Frequency response adaptation of PI controllers based on recursive least-squares process identification

The frequency domain approach to adaptive control or autotuning of proportional plus integral (PI) controllers is considered in this paper. The frequency response of the process is computed from an overparametrized recursive least-squares (RLS) model and used at sample intervals to tune the PI. By simulations it is shown that the frequency response estimates converge more quickly and are more constant in the presence of disturbances than the RLS coefficients from which they are calculated. The frequency response estimates are therefore more reliable for controller tuning. The method can handle concurrent gain variation of 50% and time constant or delay variations of 100%. The method is implemented and experimentally verified on a pilot distillation column.     

双通道卫星通信信号快速盲检测（重投）

能量谱检测在信噪比较高时具有很强的鲁棒性，但在信噪比较低时检测性能会急剧下降；而双谱检测利用非高斯噪声和信号的双谱信息，在信噪比较低时也可能获得较高的检测概率。针对卫星通信侦察环境的复杂时变、低信噪比等特点，提出了基于能量谱和双谱切片的双通道卫星通信信号快速盲检测方法。它同时利用了信号的二阶和三阶统计信息，增加了检测所可利用的信息量，有利于检测性能的提高。计算复杂度分析表明了算法的快速实时性，仿真结果验证了算法的有效性。     

Modified-filtered-u LMS algorithm for active noise control and its application to a short acoustic duct

This paper presents a new adaptive algorithm for active noise control (ANC) that can be effectively applicable to a short acoustic duct, such as the intake system of an automobile engine, where the stability and fast convergence of the ANC system is particularly important. The new algorithm, called the modified-filtered-u LMS algorithm (MFU-LMS), is developed based on the recursive filtered-u LMS algorithm (FU-LMS) incorporating the simple hyper-stable adaptive recursive filter (SHARF) to ensure the control stability and the variable step size to enhance the convergence rate. The MFU-LMS algorithm is implemented by purely experimental ways, and is applied to active control of noise in a short acoustic duct, and is validated using two experimental cases of which the primary noise sources are a sinusoidal signal embedded in white noise and a chirp signal. The experimental results demonstrate that the proposed MFU-LMS algorithm gives a considerably better performance than other conventional algorithms, such as the filtered-x LMS (FX-LMS) and the FU-LMS algorithms.     

液压机械臂控制系统的改进研究

针对矿井井筒施工中采矿液压伞钻钻孔位置和姿态调整过程自动化程度低的问题,改进了传统液压系统的控制方式,对动臂油缸和钻臂油缸进行了闭环控制。为减弱机械臂位姿变化时变负载对系统动态性能影响,设计信号校正控制模块,建立阀控液压缸数学模型,使用MATLAB中的SIMULINK仿真模块构建信号校正控制模块仿真模型。根据已有产品参数为模型添加仿真参数,经典PID控制算法和模糊自适应PID控制算法用于校正系统,当输入是步进力信号时系统响应。仿真结果对比表明:两者均可改善系统稳定性,达到预期目标;模糊自适应PID控制方法用于减少过渡期的过冲,校正信号的动态性能优于传统的PID控制方法。     

周期性机械振动主动控制算法

为提高周期性机械装置的隔振性能,减少其对底座(或地面)及周围环境的影响,采用由弹性橡胶和压电堆作动器组成的主动悬置(active control mount, ACM).针对压电堆作动器输出位移较小的情况,设计液压位移放大机构.通过对压电作动器和橡胶主簧性能的分析,建立由主动悬置构成的隔振系统的力学模型.周期性机械振动系统,其周期振动信号可用作控制同步信号,因此控制系统采用基于同步滤波-X LMS(least mean square)算法的自适应控制策略,传递到机座的残余力作为误差信号,实现对周期性机械振动系统的主动控制.计算机仿真实验结果表明,采用这种主动悬置和同步滤波-X LMS算法的主动控制系统,相对于采用普通橡胶悬置的被动系统,明显减少了对底座的力传递,减振效果明显.     

基于递推最小二乘法的地磁测量误差校正方法

针对弹体地磁测量容易受到各种误差影响而导致地磁姿态测量精度降低的问题,在分析自身误差和环境误差的基础上,对椭球模型的地磁测量误差进行建模,采用最大似然估计解算静态误差补偿参数,以解算结果为初值,通过递推最小二乘法推到补偿参数的实时更新算法,综合以上研究,形成用于地磁测量误差补偿的在线组合校正方法。仿真及实验结果表明,在接近盲区方向的最大姿态角误差小于5°,在线组合校正能够保证姿态检测系统在不同射向条件下的精度。