A novel dynamic step size LMS optimization scheme for interference reducing in FBMC-QAM

Filter bank multicarrier quadrature amplitude modulation(FBMC-QAM)will encounter inter-ference and noise during the process of channel transmission.In order to suppress the interference in the communication system,channel equalization is carried out at the receiver.Given that the con-ventional least mean square(LMS)equilibrium algorithm usually suffer from drawbacks such as the inability to converge quickly in large step sizes and poor stability in small step sizes when searching for optimal weights,in this paper,a design scheme for adaptive equalization with dynamic step size LMS optimization is proposed,which can further improve the convergence and error stability of the algorithm by calling the Sigmoid function and introducing three new parameters to control the range of step size values,adjust the steepness of step size,and reduce steady-state errors in small step sta-ges.Theoretical analysis and simulation results demonstrate that compared with the conventional LMS algorithm and the neural network-based residual deep neural network(Res-DNN)algorithm,the adopted dynamic step size LMS optimization scheme can not only obtain faster convergence speed,but also get smaller error values in the signal recovery process,thereby achieving better bit error rate(BER)performance.

Noise cancellation of a multi-reference full-wave magnetic resonance sounding signal based on a modified sigmoid variable step size least mean square algorithm

Nano-volt magnetic resonance sounding(MRS) signals are sufficiently weak so that during the actual measurement, they are affected by environmental electromagnetic noise, leading to inaccuracy of the extracted characteristic parameters and hindering effective inverse interpretation. Considering the complexity and non-homogeneous spatial distribution of environmental noise and based on the theory of adaptive noise cancellation, a model system for noise cancellation using multi-reference coils was constructed to receive MRS signals. The feasibility of this system with theoretical calculation and experiments was analyzed and a modified sigmoid variable step size least mean square(SVSLMS) algorithm for noise cancellation was presented. The simulation results show that, the multi-reference coil method performs better than the single one on both signal-to-noise ratio(SNR) improvement and signal waveform optimization after filtering, under the condition of different noise correlations in the reference coils and primary detecting coils and different SNRs. In particular, when the noise correlation is poor and the SNR<0, the SNR can be improved by more than 8 dB after filtering with multi-reference coils. And the average fitting errors for initial amplitude and relaxation time are within 5%. Compared with the normalized least mean square(NLMS) algorithm and multichannel Wiener filter and processing field test data, the effectiveness of the proposed method is verified.

Active micro-vibration control based on improved variable step size LMS algorithm

The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.

Novel LMS adaptive filtering algorithm with variable step size

By analyzing algorithms available for variable step size least mean square(LMS)adaptive filter,a new modified LMS adaptive filtering algorithm with variable step size is proposed,along with performance analysis based on different parameters.Compared with the existing algorithms through the simulation,the proposed algorithm has faster convergence speed and smaller steady state error.

Study of Variable Step Size LMS Adaptive Algorithm Based on Variable Region

This paper puts forward a new variable step size LMS adaptive algorithm based on variable region. The step size p（k） in the algorithm varies with the variation of the region of deviation e （k） to ensure the optimization of the three performance objectives including initial convergent speed, trace ability of the time-varying system and steady disregulation. The paper demonstrates the convergence of the algorithm accompanied by random noise,

基于sigmoid-sinh分段函数的变步长FxLMS算法

为改善滤波-x最小均方(filtered-x least mean square,FxLMS)算法在噪声主动控制时无法兼顾收敛速度和稳态误差的问题,提出了基于sigmoid-sinh分段函数的FxLMS(SSFxLMS)算法,并引入蚁狮算法对SFxLMS(sigmoid filtered-x least mean square)、ShFxLMS(sinh filtered-x least mean square)、SSFxLMS算法的参数进行优化。分别采用高斯白噪声和实测簇绒地毯织机噪声为输入信号,采用FxLMS、SFxLMS、ShFxLMS、SSFxLMS算法进行噪声主动控制仿真,对比分析这4种算法的性能。结果表明:与其他3种算法相比,采用SSFxLMS算法对高斯白噪声和簇绒地毯织机噪声进行控制时,误差信号的平均绝对值更小,平均降噪量与收敛速度也有大幅度提升。由此可知,SSFxLMS算法有效改善了FxLMS算法无法兼顾收敛速度和稳态误差的问题,研究结果为噪声主动控制算法设计提供了一定的参考。

变步长CMA和DD-LMS双模式切换盲均衡算法

针对经典盲均衡算法收敛速度较慢和稳态误差较大的问题,提出了一种基于变步长恒模算法(Constant Modulus Algorithm, CMA)和判决引导的最小均方(Decision Directed Least Mean Square, DD-LMS)算法的双模式切换盲均衡算法。在算法收敛初期采用CMA算法,以确保算法可以较快收敛。在收敛之后切换至DD-LMS算法,以进一步降低稳态误差。通过设定阈值来切换算法,取相邻多次迭代误差的平均值作为算法的切换值,以确保算法切换时机的合理性。另外,引入Softsign变步长函数并加入3个参数对该函数进行改进,使得Softsign变步长函数可以依据不同信道环境设定最佳参数,同时提高算法的收敛速度。仿真结果表明,在卫星通用信道条件下,所提算法的收敛迭代次数约为1 000次,稳态误差为-12 dB,在信噪比为15 dB时,误码率为1×10~(-6)。与相关算法对比,所提算法的收敛速度较高,误码率和稳态误差较低。

基于变步长凸组合LMS自适应滤波高原动态血氧检测系统设计

针对基于光电检测方法的血氧仪只能在静止状态下测试血氧饱和度(血氧)的问题,提出一种基于变步长凸组合LMS自适应滤波动态血氧检测方法。方法对原始光电描记脉搏波信号经预处理后,通过固步长与变步长LMS自适应算法组合,以三轴加速度计采集信号作为运动噪声参考信号,获得理想脉搏波信号再准确计算血氧值。在此基础上,开发了一套基于物联网的穿戴式高原血氧检测系统,经过模拟正常行走及在不同海拔条件下测试,最大误差不超过4%,验证了算法的有效性和准确性。结果显示,基于物联网的血氧监测系统对于高原旅游中高原反应的实时监测与提醒具有实际意义。

New normalized LMS adaptive filter with a variable regularization factor

A new normalized least mean square(NLMS) adaptive filter is first derived from a cost function, which incorporates the conventional one of the NLMS with a minimum-disturbance(MD)constraint. A variable regularization factor(RF) is then employed to control the contribution made by the MD constraint in the cost function. Analysis results show that the RF can be taken as a combination of the step size and regularization parameter in the conventional NLMS. This implies that these parameters can be jointly controlled by simply tuning the RF as the proposed algorithm does. It also demonstrates that the RF can accelerate the convergence rate of the proposed algorithm and its optimal value can be obtained by minimizing the squared noise-free posteriori error. A method for automatically determining the value of the RF is also presented, which is free of any prior knowledge of the noise. While simulation results verify the analytical ones, it is also illustrated that the performance of the proposed algorithm is superior to the state-of-art ones in both the steady-state misalignment and the convergence rate. A novel algorithm is proposed to solve some problems. Simulation results show the effectiveness of the proposed algorithm.

Lattice structure adaptive IIR notch filter based on least square kurtosis

A new variable step-size algorithm for a second-order lattice form structure adaptive infinite impulse response （IIR） notch filter to detection and estimation frequency of sinusoids in Gaussian noises is proposed. Utilizing least square kurtosis of output signals as a cost function, the new gradient-based algorithm to update frequency of the adaptive IIR notch filter and the new variable step-size algorithm are given. The computer simulation results show that the proposed algorithm has better ability in suppressing colored Gaussian noises and better accuracy in estimating parameters at low SNR than previous algorithms.

一种用于毫米波探测系统的变步长LMS-AGC方案

在毫米波探测系统中,自动增益控制(AGC)是信号处理系统的关键技术之一,其作用是应对大动态范围的中频信号接收。本文阐述了一种数字自动增益控制(DAGC)的实现方案,通过对中频信号的数字采样进行处理,采用基于平均功率参量的变步长LMS-AGC自适应算法,将自适应滤波器的变步长思想应用到AGC方案中,并对其步长算法进行优化。通过对比普通功率检测自动增益控制和固定步长LMS-AGC方案,本文提出的变步长LMS-AGC方案具有更快的收敛速度和更小的稳态误差。

箕舌线变步长LMS/F算法数字域自干扰消除研究

自干扰消除技术是实现带内全双工(IBFD)通信的重要前提,其中数字域自干扰消除是带内全双工通信系统中硬件复杂度最低、灵活性最高的自干扰消除技术,并且是自干扰消除的最后一道防线。然而,其消除能力仍需提升,主要是如何处理收敛速度和稳态精度之间的平衡,并且还要具备突变信道的自适应能力。文章提出了一种新的全双工系统的数字自干扰消除方法,发射链路采用数字、模拟预失真技术消除功率放大器的非线性失真,使用辅助接收链获取发射链路信号副本,在数字域中利用重构自干扰信号副本消除接收信号残余自干扰信号和功率放大器残余非线性失真,并通过在接收链与辅助接收链之间共用一个振荡器消除部分接收机相位噪声。仿真表明,该方法与已有变步长LMS消除方法相比,在信噪比为5 dB的条件下,能够在提高收敛速度的同时获得优于变步长LMS方法的消除能力。

基于对称非线性函数的变步长LMS自适应滤波算法

为解决自适应最小均方误差(least mean squares,LMS)滤波算法难以平衡稳态误差和收敛速度的问题,提出了基于对称非线性函数的变步长LMS自适应滤波算法。通过自变量取绝对值、叠加非线性拉伸量改进Sig-moid函数,构造一个对称非线性函数用于刻画步长因子与稳态误差的非线性关系。该对称非线性函数具有能够根据误差动态调整步长、更快达到收敛状态的特点。根据构造的对称非线性函数和输入信号功率生成归一化变步长因子,解决噪声逐级放大的问题,进一步提高算法的滤波效果同时,加速收敛。实验表明:该算法在低信噪比、信噪比变化、信号频率变化、滤波器阶数变化、延迟采样点数变化条件下均具有更好的滤波效果、更优的稳定性和更快的收敛速度。

基于双曲正弦函数设计变步长的LMS算法

针对非高斯环境下传统变步长LMS(Variable step-size least mean square,VSS-LMS)算法性能不佳的问题,基于传统的VSS-LMS算法利用双曲正弦函数构建变步长的更新策略,提出一种基于双曲正弦函数的变步长LMS算法。并在理论上分析了新提出VSS-LMS算法的收敛性与算法复杂度,并给出在不同输入信号时对两种特性的线性系统的VSS-LMS算法的辨识结果,且每次仿真中都在不同分布的非高斯噪声下进行。结果表明,提出的算法相比Log-NLMS算法和改进G-SVSLMS算法,新提出的VSS-LMS算法具有更快的收敛速度和较好的稳态特性,且稳态误差趋于理论的SNR。

一种适用于复杂电网下的高精度锁相环

针对现有并网逆变器在电网电压谐波复杂的情况下难以精确锁定电压频率和相位的问题,结合传统锁相环结构与自适应算法的理论提出一种高精度的锁相方法。依据对传统锁相环在复杂电网下的弊端分析,确定了锁相环采用传统低通滤波器、带通滤波器时的滤波效果与延时的矛盾关系。根据最小均方算法的高自适应特性与快速跟踪特点,结合锁相环中坐标变换公式,确定算法的输入输出以及权重矩阵,构建基于派克变换的最小均方算法矩阵模型。通过将所需的电压正序分量设置为不断更新的权重矩阵来进行高精度的滤波,同时通过引入均方瞬时误差和自相关估计均值来进行变步长控制,提高滤波更新速度,以适用于复杂程度不确定的电网电压进行自适应滤波锁相。对仿真和样机实验的结果表明:采用基于派克变换的最小均方算法的改进型双二阶广义积分锁相方法,可以在不降低动态响应速度的前提下,提高锁相环的滤波效果,进而提高并网逆变器在电网畸变严重场景下锁定电压频率和相位的跟踪精度。

基于小波变换的有源噪声控制算法研究

在传统的有源噪声控制系统中,原始噪声信号是获得参考噪声信号的必要条件,当原始噪声信号的信息不可获知时,有源噪声控制系统失效。为解决该问题,设计基于小波变换的有源噪声控制系统,利用小波阈值法对含噪语音信号进行处理,得到与原始噪声信号相关的参考噪声信号。在此基础上,对传统的有源噪声控制算法LMS进行改进,提出基于Sigmoid函数的γSLMS算法和C-γSLMS凸组合算法,解决LMS算法无法同时兼顾收敛速度和稳态误差的问题,为解决有源噪声控制问题提供新的解决方案。

基于改进的双曲正切函数变步长LMS算法

为了改进现有的变步长最小均方误差(least mean square,LMS)算法在低信噪比时性能较差的缺陷,提出了一种基于改进的双曲正切函数的变步长LMS算法,从理论分析和仿真实验两方面讨论了引入参数对算法收敛性、跟踪性、稳定性的影响及算法的抗干扰性。理论分析和仿真实验表明该算法在高低信噪比时均具有较快的收敛速度和跟踪速度以及较小的稳态误差和稳态失调,并且在低信噪比时该算法的收敛性、跟踪性、稳态性均优于其他多种变步长算法。

自适应变步长LMS滤波算法及分析

为了提高最小均方(LMS)自适应滤波算法的性能,通过建立步长因子与误差信号之间的非线性函数关系,提出一种自适应变步长LMS算法。该算法具有初始阶段和时变阶段步长自适应增大和稳态阶段步长很小的特点,消除了不相关噪声的影响,并且进一步克服了Sigmoid函数变步长LMS算法在自适应稳态阶段步长取值偏大的缺陷,计算机仿真结果与理论分析相一致,证实该算法优于传统算法。

一种新的变步长LMS自适应滤波算法

本文通过建立步长因子μ与误差信号之间的非线性函数关系,提出了一种新的变步长LMS(LeastMean Square)算法.该算法具有初始阶段和未知系统时变阶段步长自动增大而稳态时步长很小的特点,且克服了S函数变步长LMS算法(简称SVSLMS算法)在自适应稳态阶段μ(n)取值偏大的缺陷.理论分析和计算机仿真结果表明该算法的性能优于SVSLMS算法.

一种改进的解相关LMS自适应算法

针对变步长LMS自适应滤波算法在输入信号高度相关时,收敛速度下降导致性能下降的问题,提出了一种改进的解相关LMS自适应算法,该算法引入解相关原理和归一化处理,用输入向量的正交分量来更新滤波器权系数,有效加快了算法的收敛速度,且稳态误差小,使得算法在有色输入和大范围的动态输入下都能保持良好性能.