语音去噪LMS自适应滤波器算法的改进

对LMS自适应算法进行了详细的性能分析与讨论,针对LMS算法运算较复杂、适应性较弱、稳定性差的缺点提出了一种HLMS(混合LMS)算法.建立了自适应噪声抵消系统,利用MATILAB软件对食堂、体育馆两处的录音信号进行计算机语音去噪仿真分析.实验结果表明,两种自适应方法均能有效抑制各种噪声污染,提高语音信噪比为60%～8...     

基于MIMO—OFDM系统的信道估计算法分析

MIMO-OFDM技术是未来无线通信系统的研究热点。信道估计是估计出信道的时域或频域响应,对接收到的数据进行校正与恢复,是实现MIMO-OFDM系统优良的传输性能的重要环节。对MIMO-OFDM系统的多种信道估计算法进行了全面深入地探讨,重点分析和比较了基于LS和MMSE的非盲信道估计算法、盲信道估计算法和半盲信道估计算法,并提出了未来信道估计算法的研究方向。     

Sparse LMS/F algorithms with application to adaptive system identification

Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advantages of both least mean square (LMS) and least mean fourth (LMF). The advantage of LMS is fast convergence speed while its shortcoming is suboptimal solution in low signal‐to‐noise ratio (SNR) environment. On the contrary, the advantage of LMF algorithm is robust in low SNR while its drawback is slow convergence speed in high SNR case. Many finite impulse response systems are modeled as sparse rather than traditionally dense. To take advantage of system sparsity, different sparse LMS algorithms with l_p‐LMS and l₀‐LMS have been proposed to improve adaptive identification performance. However, sparse LMS algorithms have the same drawback as standard LMS. Different from LMS filter, standard LMS/F filter can achieve better performance. Hence, the aim of this paper is to introduce sparse penalties to the LMS/F algorithm so that it can further improve identification performance. We propose two sparse LMS/F algorithms using two sparse constraints to improve adaptive identification performance. Two experiments are performed to show the effectiveness of the proposed algorithms by computer simulation. In the first experiment, the number of nonzero coefficients is changing, and the proposed algorithms can achieve better mean square deviation performance than sparse LMS algorithms. In the second experiment, the number of nonzero coefficient is fixed, and mean square deviation performance of sparse LMS/F algorithms is still better than that of sparse LMS algorithms. Copyright © 2013 John Wiley & Sons, Ltd.     

Reduce‐complexity fuzzy‐inference‐based iterative multiuser detection for wireless communication systems

In recent years, combining multiuser detection and intelligence computer scheme have received considerable attention. In this paper, adaptive fuzzy‐inference multistage matrix wiener filtering (FI‐MMWF) techniques, based on the minimum mean‐square error criterion, are proposed for ultra‐wideband (UWB) impulse radio communication systems. These FI‐MMWF‐based algorithms employ a time‐varying fuzzy‐inference‐controlled filter stage. Consequently, the proposed approaches accomplish a substantial saving in complexity without trading off the system performance and dynamic‐tracking characteristic. In addition, the fuzzy‐logic‐controlled matrix conjugate gradient algorithm is adopted to reduce the system complexity without trading off the bit‐error‐rate (BER). Simulations are conducted to evaluate the convergence and tracking behavior of the proposed MMWF algorithm, and the BER of the time‐hopping‐UWB system in a realistic UWB channel is investigated. Copyright © 2011 John Wiley & Sons, Ltd.     

一种改进的稀疏多径信道盲辨识算法

针对无线多径稀疏信道,利用信道有效近似思想,提出了一种改进的基于矩阵外积分解的信道盲辨识与盲均衡算法。算法首先利用改进的VIA信道阶数估计准则,对多径稀疏信道“有效部分”的阶数进行精确估计,然后利用改进的矩阵外积分解算法估计出信道冲激响应的“有效部分”,最后利用该估计结果对接收数据进行反卷积运算,恢复出发送信号。为了降低噪声以及信道冲激响应中的“零抽头”部分对信道盲辨识性能的影响,本算法对噪声方差估计方法进行了改进,提高了算法在中、低信噪比条件下的盲辨识性能。与现有算法相比,本算法不仅降低了对信噪比的要求,而且克服了基于LC准则的子空间算法(SSA, Subspace Algorithm)的相位偏转问题,其中噪声方差的估计方法也可应用于信噪比估计技术。仿真实验以及对SPIB微波信道测试结果验证了本文算法的有效性。      

Adaline神经网络随机逼近LMS算法的仿真研究

基于最小二乘（LMS）统计算法的自适应线性元件（Adaline）神经网络是非线性分类的重要工具之一。从计算机仿真的角度研究随机逼近LMS学习方法的特点，从步长设置、收敛性、收敛速度、算法抗噪性、判断的准确率等多个参量评估随机逼近法的性能。仿真结果表明，对于不同的初始步长设置，神经元完成学习任务的训练时间不同；在保证学习收敛性的前提下，步长越大，收敛速度越快，但收敛的稳定性变差。权矢量的初始值设置对学习的收敛性没有影响。     

Learning Behaviors of Stochastic Gradient Radial Basis Function Network Algorithms for Odor Sensing Systems

Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF‐SVD‐SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine‐tuning of centers and widths still shows ill‐behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center‐gradient variance of the RBFN‐SVD‐SG algorithm. We found analytically that the steady‐state weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center‐gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady‐state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.     

一种适用于强脉冲噪声下的对数型恒模盲均衡算法

针对脉冲噪声下恒模算法(Constant Modulus Algorithm,CMA)失败的问题,通过分析脉冲噪声的影响,提出了一种基于最小均方（Least Mean Square,LMS）准则的对数型恒模算法（Logarithmic-type CMA,LT-CMA）。LT-CMA利用对数函数的非线性变换特性自适应地抑制强脉冲噪声对误差函数的影响,并且利用l2-范数进行信号归一化处理以增强算法的稳健性。仿真结果表明,所提出的LT-CMA可以适应于高斯噪声环境和脉冲噪声环境;与经典自适应均衡算法相比,在收敛速度和稳健性两方面上,所提出的LT-CMA都有显著的提升。     

一种基于双通道自适应噪声对消的语音增强法

在自适应噪声对消(ANC)中,本文根据主辅通道噪声间的相关性,提出了一种自适应滤波语音增强算法。在低信噪比(-10～0dB)白噪声条件下,文中研究了辅助通道采集的噪声有无"串音"两种情况下的语音增强效果。研究表明:在无串音和有串音两种条件下,本文算法增强语音的信噪比分别比NLMS算法提高约14dB和5dB,PESQ_MOS得分分别比NLMS算法提高约1.13和0.83,同时增强语音的听觉质量得到了极大地改善。     

室内可见光通信信道估计的研究

要:建立了室内可见光通信系统模型,针对其信道冲击响应,分别采用最小二乘(LS)估计方法和最小均方差(MMSE)估计方法对信道进行了理论分析,并且通过计算机仿真,对两种不同的估计方法性能进行了比较,最后在此基础上,分别对抽头长度和训练序列长度对信道估计的性能影响进行了仿真验证。实验结果表明:在相同信噪比下,MMSE方法的性能优于LS方法,但是计算量比LS方法复杂,而且LS方法性能依赖于噪声的影响,信噪比越高,LS估计方法的性能越好。同时抽头数越多,训练序列越长,信道估计的性能越好,但也会增加计算的复杂度。     

基于均衡代价函数的信道阶数盲估计算法

针对信道阶数估计问题,利用单输入多输出(Single-Input Multiple-Output,SIMO)有限冲激响应(Finite Impulse Response,FIR)信道的结构特点和输入/输出信号的统计特征,提出了一种基于均衡代价函数的信道阶数盲估计算法.首先计算了归一化最小二乘均衡(Normalized Least Squares Equalization,NLSE)代价函数在理想条件下的理论渐近值,并指出其拐点与信道阶数之间的对应关系.然后分析了NLSE代价函数在实际条件下的近似值.最后引入了拐点优化因子,提出了一种基于NLSE代价函数拐点检测的信道阶数估计算法.理论分析和仿真结果表明,在信噪比(Signal-to-Noise Ratio,SNR)较低和信道首尾系数较小的情况下,该算法比现有其它方法具有更强的鲁棒性,可以获得更小的接收信号均衡误差.     

稳健的多通道SAR/GMTI通道盲均衡算法

对于实际的多通道合成孔径雷达（Synthetic Aperture Radar,SAR）系统,各接收通道响应之间不可避免地存在着一定程度的幅度和相位误差,为了得到较为满意的地面动目标显示（Ground Moving Target Indication,GMTI）性能,通常都会在杂波抑制之前对通道间的幅度相位误差进行有效地校正.本文在基于回波数据相关矩阵特征分解的通道盲均衡算法基础上,结合降维处理技术及中值估计方法,提出一种稳健的多通道SAR/GMTI通道盲均衡算法.实测数据实验结果表明：与原通道盲均衡算法相比,本文所提算法不但收敛速度快,而且算法的有效性不受样本集中目标信号的影响.     

基于LMS算法的数据采集系统动态传输特性研究

提出了一种基于最小均方(LMS)算法的数据采集系 统动态传输特性获取方法。在建立数据采集系统FIR滤波器模 型的基础上,以正弦信号为系统输入,以采集处理后的实验数据为系统输出,利用LMS算法 反推系统的通 道模型参数,计算系统对正弦信号的幅度衰减、相位延迟和噪声参杂3个动态传输特性;在 方法研究的基 础上,搭建了相关实验验证平台,分别进行了系统仿真和硬件验证实验。仿真实验中,3个 动态传输特性的识别精度均在98%以上,证明了算法在理论上的可行性;硬件 验证实验中,传输特性的平均识别精度均在96%以上,表明本文所提数据采集系统动态传 输特性获取方法 有效可行,且具有很好的数据动态传输特性识别精度。     

扩频系统中基于Kalman滤波的最优干扰抑制技术

针对扩频系统中的干扰抑制问题,本文首先将其建模为附加约束的最小化均值输出能量(MMOE)问题,然后借助正交分解将约束MMOE转化为无约束最小均方误差(MMSE),接着通过选择合适的状态变量、建立合适的状态方程和观测方程得到盲Kalman滤波(BKF)算法,最后分析了BKF算法性能.研究表明:BKF的收敛性能与输入相关矩...     

DFT/LMS算法在DSSS中的应用及性能分析

本文分析了直接序列扩频(DSSS)系统中最小错误概率(MPE)意义下的最优滤波器,并依据矩阵求逆引理证明最小均方误差(MMSE)意义下的最优滤波——维纳滤波也是MPE意义下的最优滤波。在DSSS中应用自适应滤波,无须先验已知扩频码的码型和干扰的统计特性,就能一并完成解扩以及有效抑制干扰。离散傅立叶变换/最小均方(DFT/LMS)算法的收敛速度远快于LMS算法,而运算量、稳健性与LMS算法基本相同。基于DFT/LMS算法的自适应滤波大大简化DSSS系统接收机的设计,显著增强系统抗干扰能力,具有很强的实用性。     

不相关高斯输入情况下LMS滤波器的收敛性分析

在分析最小均方自适应滤波器(LMS AF)均方误差(MSE)的收敛性时,文献常用统计自相关矩阵代替瞬时自相关矩阵以简化分析,由此得出的收敛条件比较粗糙。本文指出:不相关高斯输入情况下,无需如上简化,可依据高斯阶矩因式分解定理得到确切的MSE收敛条件,相应的失调表式能更准确地预报失调。     

基于频域特性的时间交替模数转换系统采样时间误差校正算法

时间交替模数转换器(Time-Interleaved ADC,TIADC)通道间的采样时间相对误差严重影响了系统的无杂散动态范围(Spurious-Free Dynamic Range,SFDR).为校正采样时间相对误差,本文基于TIADC输出与模拟输入信号之间的频域关系,提出一种通过消除输出信号中的误差来校准TIADC的算法.该算法在对输出信号频率表达式进行泰勒近似的基础上构建理想输出信号,并采用最小均方差(LMS)算法来估算时间误差,旨在降低硬件设计的复杂度,提高误差校正的精确度.仿真和验证结果表明该校正算法很容易扩展到多通道,并且可以将输出频谱的SFDR提高约47dB.     

Stable adaptive channel estimation method under impulsive noise environments

Channel estimation is one of the key technologies for ensuring reliable wireless communications under impulsive noise environments. This paper studies robust adaptive channel estimation methods for mitigating harmful impulsive noises, which are described as alpha‐stable (α ‐stable) distribution models. Traditional adaptive channel estimation using the second‐order statistics based least mean square (SOS‐LMS) algorithm does not perform well under α ‐stable noise environments, even though it was considered one of attractive approaches for estimating channels in the case of Gaussian noises. Unlike the traditional SOS‐LMS algorithm, in this research, we propose a stable sign‐function‐based LMS algorithm, which can mitigate the impulsive noises. Specifically, we first construct the cost function with minimum ℓ ₁‐norm error criterion and then derive the updating equation of the proposed algorithm. Compared with the traditional SOS‐LMS, the effectiveness of the proposed algorithm is validated via Monte Carlo simulations in various α ‐stable noise scenarios. Copyright © 2015 John Wiley & Sons, Ltd.     

Analysis and design of joint CFO/channel estimate techniques for a cooperative STBC‐OFDM system

This paper studies the joint estimation technique of carrier frequency offset (CFO) and channel information for a distributed decode‐and‐forward (DF) cooperative space‐time block‐coded (STBC) orthogonal frequency division multiplexing (OFDM) system. For the considered relay system, we provide theoretical analysis of the effects upon the output signal‐to‐noise ratio (SNR), which is caused by the CFO/channel estimation error. Based on the provided analytical results, a joint CFO/channel estimation scheme is then developed, where the CFO estimate is achieved by a multiple‐dimensional linear search algorithm. Furthermore, we propose an alternative estimation solution with iteration approach being designed for the CFO estimation prior to the channel estimation. In contrast to the former estimator, the iterative method enjoys the advantage of the substantially reduced implementation complexity without sacrificing the estimate performance. The conducted computer simulation results verify the effectiveness of the proposed schemes.     

基于冲激响应加窗法的CO-OFDM系统信道估计研究

针对相干光正交频分复用(CO-OFDM)系统中光纤的色散和信号传输过程中噪声对系统可靠性的降低,提出了信道的冲激响应加窗(IRM,impulse response processed with window)算法,在最小二乘(LS)算法的基础上,通过时域加窗将信道冲激响应长度以外的噪声滤除,保证了信道冲激响应长度在OFDM保护间隔之内。在算法复杂度提高并不大的前提下,IRW算法系统误码率(BER)比LS算法降低了近1个数量级。仿真结果表明,在256个子载波的CO-OFDM系统中,BER为10-4时,IRW算法对系统信噪比(SNR)的要求比LS算法低了近2.5dB,而只比线性最小均方误差(LMMSE)算法高0.7dB;并且,IRW算法的复乘次数仅比LS算法高8倍,而比LMMSE算法的复乘次数低32倍。