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.

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.

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.

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,

Joint receiving mechanism based on blind equalization with variable step size for M-QAM modulation

The problem of inter symbol interference（ ISI） in wireless communication systems caused by multipath propagation when using high order modulation like M-Q AMis solved. Since the wireless receiver doesn＇t require a training sequence,a blind equalization channel is implemented in the receiver to increase the throughput of the system. To improve the performances of both the blind equalizer and the system,a joint receiving mechanismincluding variable step size（ VSS） modified constant modulus algorithms（ MC-MA） and modified decision directed modulus algorithms（ MD DMA） is proposed to ameliorate the convergence speed and mean square error（ MSE） performance and combat the phase error when using high order QAM modulation. The VSS scheme is based on the selection of step size according to the distance between the output of the equalizer and the desired output in the constellation plane. Analysis and simulations showthat the performance of the proposed VSS-MCMA-MD DMA mechanismis better than that of algorithms with a fixed step size. In addition,the MCMA-MDDMA with VSS can performthe phase recovery by itself.

Finite-difference modeling with variable grid-size and adaptive time-step in porous media

Forward modeling of elastic wave propagation in porous media has great importance for understanding and interpreting the influences of rock properties on characteristics of seismic wavefield. However,the finite-difference forward-modeling method is usually implemented with global spatial grid-size and time-step; it consumes large amounts of computational cost when small-scaled oil/gas-bearing structures or large velocity-contrast exist underground. To overcome this handicap,combined with variable grid-size and time-step,this paper developed a staggered-grid finite-difference scheme for elastic wave modeling in porous media. Variable finite-difference coefficients and wavefield interpolation were used to realize the transition of wave propagation between regions of different grid-size. The accuracy and efficiency of the algorithm were shown by numerical examples. The proposed method is advanced with low computational cost in elastic wave simulation for heterogeneous oil/gas reservoirs.

Variable Step Filtered-X Least Mean Square Algorithm Based on Piecewise Logarithmic Function

In order to improve the problem that the filtered-x least mean square(FxLMS)algorithm cannot take into account the convergence speed,steady-state error during active noise control.A piecewise variable step size FxLMS algorithm based on logarithmic function(PLFxLMS)is proposed,and the genetic algorithm are introduced to optimize the parameters of logarithmic variable step size FxLMS(LFxLMS),improved logarithmic variable step size Films(IFxLMS),and PLFxLMS algorithms.Bandlimited white noise is used as the input signal,FxLMS,LFxLMS,ILFxLMS,and PLFxLMS algorithms are used to conduct active noise control simulation,and the convergence speed and steady-state characteristic of four algorithms are comparatively analyzed.Compared with the other three algorithms,the PLFxLMS algorithm proposed in this paper has the fastest convergence speed,and small steady-state error.The PLFxLMS algorithm can effectively improve the convergence speed and steady-state error of the FxLMS algorithm that cannot be controlled at the same time,and achieve the optimal effect.

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.

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.

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。

APF中一种改进的变步长LMS自适应谐波检测算法

在有源电力滤波器(Active Power Filter,APF)的低信噪比(Signal Noise Ratio,SNR)环境下,为了提高变步长最小均方(Least Mean Square,LMS)自适应算法对谐波电流检测的跟踪速度及精度,提出改进的变步长LMS算法。该算法在MVSS-LMS算法的基础上,增加历史误差的遗忘加权和估计并控制步长更新,动态控制步长更新范围,采用滑动窗遗忘加权减小了计算复杂度。同时,对改进算法性能进行稳定性分析。实验结果表明,该算法不仅具有较快的动态响应速度,而且在APF的低信噪比情况下,稳态误差有所减小,具有较高的抗干扰能力,谐波电流检测效果较好。

一种变步长Proportionate NLMS自适应滤波算法及其在网络回声消除中的应用

Proportionate自适应算法利用稀疏冲激响应的结构特征,极大地加速了算法的收敛速度.但是快速收敛与低稳态失调是一对矛盾的需求,固定步长算法必需折中选择一个步长参数来满足应用的要求.本文提出了一种适用于Proportionate算法的变步长方法,有效解决了收敛速度和稳态失调之间的矛盾.所提的算法首先利用最小干扰原理,得到了一个Proportionate NLMS算法的推导;进而将干扰信号考虑进算法的系数更新过程,通过在每一步迭代中用后验误差去补偿干扰信号的负面作用,得到一个新的优化准则;最后利用这个准侧,推导出了一个适用于Proportionate算法的步长调节方法.仿真实验验证了本文方法的有效性.

一种变步长LMS算法及其Matlab仿真

对变步长自适应滤波算法进行了讨论,对VS-LMS算法进行了改进,建立了步长因子μ与误差信号e(n)之间另一种新的非线性函数关系.理论分析和计算机仿真结果表明,该关系不仅具有原有算法收敛速度快的优点,而且在低信噪比环境下比原有算法具有更好的抗噪声性能.

一种基于Lorentzian函数的变步长LMS自适应滤波算法

为了改善传统的LMS类算法存在的缺点,提高算法的收敛速度和跟踪性能,提出了一种基于Lorentzian函数的变步长LMS自适应滤波算法。该算法采用Lorentzian函数来建立估计误差e(n)和步长因子μ(n)之间的非线性关系,以达到提高收敛速度和改善跟踪性能的目的。计算机仿真表明,该算法与其他LMS类自适应滤波算法相比,在收敛速度和跟踪性能上都有较大的提升。

使用变步长频域LMS算法的自适应Volterra均衡器

Volterra均衡器能够有效地克服卫星信道的非线性失真,但由于非线性均衡器的输入矩阵特征值扩展严重,使得自适应过程收敛缓慢。为克服这个缺点,提出应用变步长的频域LMS算法对Volterra均衡器的权值系数进行自适应更新。算法利用正交变换降低输入序列的相关性,同时动态地调整迭代步长提高均衡器的收敛速度。仿真结果表明与时域算法相比,均衡器的收敛速度提高了25倍左右;均衡器收敛后纠了信号的幅度和相位失真。

基于动量LMS算法的变步长次级路径在线建模ANC系统

针对ANC系统收敛速度慢、稳定性差、次级通道建模精度低,以及降噪性能弱的问题,提出了动量LMS算法。采用宽带前馈型主动噪声控制的结构进行系统设计,有效处理非周期低频噪声信号;设计动量LMS算法,对次级通道路径进行实时在线建模;提出利用建模精度ΔS的变化来决定步长变化的2种方法,并在LMS算法基础上增加了2个动量项。仿真结果表明,所提2种方法有效地提高了ANC系统的降噪性能、算法的收敛速度及建模精度,降噪量最高分别可达17 dB,16 dB。

变步长解相关Volterra LMS算法

Volterra滤波器的非线性使输入向量自相关矩阵包含了输入信号的高阶统计量,导致矩阵特征值扩展很大,因此LMS算法收敛速度一般很慢。从降低输入信号的相关性出发,提出了一种变步长解相关VolterraNLMS算法。解相关能显著加快LMS算法的收敛速度,变步长能够改善算法的稳态性能,两者的有机结合,能明显改善算法性能。仿真结果表明,在不同输入信号相关性情况下,该算法有更好的收敛速度和稳态性能。

基于变步长LMS的APF谐波电流检测方法

有源电力滤波器(active power filer,APF)谐波电流检测环节中,针对传统定步长最小均方误差(least mean square,LMS)检测算法在稳态精度和响应速度上的缺陷,提出了一种自适应变步长LMS谐波检测算法。该算法利用历史误差数据,计算误差的功率加权并以之作为步长调节的主控因子,对步长进行调节。扰动因子的引入能够对噪声进行抑制。最后将该算法用于APF的谐波检测。仿真及实验表明,该算法不仅具有快速的动态响应特性,同时又具有较高的检测精度,能够满足APF谐波电流检测的需求。

基于POD-αATS的油浸变压器瞬态温升降阶自适应变步长计算方法

针对油浸式电力变压器瞬态温升计算效率过低的问题,该文提出本征正交分解-αATS(proper orthogonal decomposition-adaptive time stepping based onαfactor,POD-αATS)降阶自适应变步长瞬态计算方法。首先,推导变压器绕组瞬态温升计算的有限元离散方程;其次,采用POD降阶算法改善传统瞬态计算中存在的条件数过大及方程阶数过高的问题;同时对于瞬态计算中的时间步长选择问题,提出适用于非线性问题的αATS变步长策略;然后,为验证方法的有效性,基于110 kV油浸式电力变压器绕组的基本结构建立二维八分区数值计算模型,同时将计算结果与基于110 kV绕组的温升实验结果进行对比。数值计算及实验结果表明,所提算法与全阶定步长算法在流场和温度场中的精度几乎相同,且流场计算效率提升约45倍,温度场计算效率提升约38倍,计算速度得到显著提高。这一点在温升实验中同样得到验证,说明该文所提算法的准确性、高效性及一定的工程实用性。

脉冲噪声下AUV水声通信盲均衡算法模拟仿真

针对存在琢稳定分布噪声的水声信道中传统常数模盲均衡算法(constant modulus algorithm,CMA)无法稳定收敛的问题,提出一种基于模拟退火粒子群算法优化的变步长分数低阶盲均衡算法(SAPSO-VSS-FLOSCMA),该算法将FLOSCMA的代价函数作为SAPSO算法的适应度函数,利用SAPSO算法搜索全局最优解代替盲均衡器的初始权向量,降低了FLOSCMA算法的稳态误差,并采用双曲正切函数调节FLOSCMA算法的步长因子加快算法的收敛速度。水声信道仿真结果表明,与CMA和FLOSCMA算法相比,SAPSO-VSS-FLOSCMA算法在收敛速度和稳态误差方面均得到提高,且误码率更低。