一种新的带窗重叠自适应滤波器

基于一种带窗重叠自适应滤波器,将重叠滤波思想引入LMS算法。利用重叠滤波的平滑性,将加窗重叠滤波和LMS算法相结合,给出了窗加权重叠LMS(WO-LMS)算法。与传统的LMS算法相比,WO-LMS算法既提高了收敛速度又可以得到较低的稳态均方误差。理论分析了算法的收敛性,通过与LMS算法的比较,验证了WO-LMS算法的优越性。     

基于FPGA和符号LMS算法的自适应滤波器设计

为了减少实现基于FPGA和LMS算法的自适应滤波器过多消耗硬件资源的问题,提出了符号LMS算法,通过降低乘法运算的次数来提高自适应滤波器的运行速度,并使用流水线技术进行优化。软件仿真验证了符号LMS算法的可行性,硬件仿真证实了采用该算法和流水线技术的自适应滤波器的优越性。     

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。     

自适应最小二乘格型算法在天线阵列中的应用

在天线阵列中对天线阵列信号分析的算法有LMS和RLS等，主要介绍了自适应最小二乘格型算法LSL对天线信号进行分析，并对算法过程展开介绍。     

Adaptive sparse system identification using normalized least mean fourth algorithm

Normalized least mean square (NLMS) was considered as one of the classical adaptive system identification algorithms. Because most of systems are often modeled as sparse, sparse NLMS algorithm was also applied to improve identification performance by taking the advantage of system sparsity. However, identification performances of NLMS type algorithms cannot achieve high‐identification performance, especially in low signal‐to‐noise ratio regime. It was well known that least mean fourth (LMF) can achieve high‐identification performance by utilizing fourth‐order identification error updating rather than second‐order. However, the main drawback of LMF is its instability and it cannot be applied to adaptive sparse system identifications. In this paper, we propose a stable sparse normalized LMF algorithm to exploit the sparse structure information to improve identification performance. Its stability is shown to be equivalent to sparse NLMS type algorithm. Simulation results show that the proposed normalized LMF algorithm can achieve better identification performance than sparse NLMS one. Copyright © 2013 John Wiley & Sons, Ltd.     

基于粒子群优化算法思想的组合自适应滤波算法

根据粒子群优化(PSO)算法的社会心理学指导思想并结合自适应FIR滤波器的特点,设计了合适的惯性项、认知项与社会项表达式,并将之应用于组合自适应滤波器的子自适应滤波器更新中,提出了基于PSO算法思想的组合自适应滤波算法,分析了新算法的计算复杂度。理论分析与不同条件下的自适应系统辨识仿真结果表明,新算法可以在不明显提高计算量的条件下较好地平衡自适应滤波器的稳态失调与跟踪能力,其收敛性能优于其它几种较新的LMS算法。     

基于NLMS的LFM信号自适应校正研究

该文针对雷达对抗中的LFM信号进行了自适应校正的研究,用NLMS算法对LFM信号的校正进行了建模与理论推导,求出了最优步长与最小误差的具体表达式,并进行了计算机仿真验证,表明了该理论分析的指导价值。     

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...     

逐级最优自适应滤波算法

本文提出了应用整体最小二乘技术的递推自适应滤波算法。论证说明了算法的收敛特性。从最小二乘到整体最小二乘的逐级优化使滤波结构具有灵活的配置。通过高级EDA软件SPW的模拟，验证了算法在自适应杂波抑制中的有效性．整个算法具有相对高性能和易实现的特点。     

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。     

Generalized Robust Multichannel Frequency‐Domain LMS Algorithms for Blind Channel Identification

Recently, several noise‐robust adaptive multichannel LMS algorithms have been proposed based on the spectral flatness of the estimated channel coefficients in the presence of additive noise. In this work, we propose a general form for the algorithms that integrates the existing algorithms into a common framework. Computer simulation results are presented and demonstrate that a new proposed algorithm gives better performance compared to existing algorithms in noisy environments.     

基于自适应算法的四波混频抑制技术

介绍了一种新的基于自适应算法的四波混频抑制技术。在四通道波分复用系统中,观察到了较为严重的四波混频现象,产生的新波长光中,光信噪比(O SNR)最高高达35 dBm。基于最小均方算法(LMS)的自适应滤波器可自动区分信号光波长和新产生的FWM光波长,并对FWM光进行幅度调制。在输出信号光的脉冲重复频率为10kHz,脉宽为500ns,单脉冲能量为30μJ的条件下,通过实验验证了该自适应滤波器可有效的抑制四波混频效应,同时也改善了系统的误码率(BER)。     

自适应加权FIR-Myriad混合滤波算法

针对FIR滤波器滤除脉冲噪声以及加权Myriad滤波器滤除高斯噪声的不足,提出基于FIR滤波器和加权WMy滤波器有效组合的一类新的非线性滤波器FIR-WMyH滤波器.利用神经网络中的反向传播算法,在均方误差准则下,推导了一个基于统计梯度的自适应算法.基于稳定α分布脉冲噪声模型下的仿真结果说明了该算法的良好的性能.     

奇异值分解抑制窄带干扰的非线性方法

提出了一种联合奇异值分解(SVD)和非线性滤波抑制直接序列扩频通信(DS/SS)中单音干扰的方法,建立了相应的系统模型。并对传统的线性双边滤波器的信噪比增进因子和误码率进行了比较。仿真结果表明,奇异值分解的非线性滤波方法对干扰有很强的抑制能力,较好地改善了系统的性能。     

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.     

Adaptive neuro fuzzy inference system-based power estimation method for CMOS VLSI circuits

Recent advancements in very large scale integration (VLSI) technologies have made it feasible to integrate millions of transistors on a single chip. This greatly increases the circuit complexity and hence there is a growing need for less-tedious and low-cost power estimation techniques. The proposed work employs Back-Propagation Neural Network (BPNN) and Adaptive Neuro Fuzzy Inference System (ANFIS), which are capable of estimating the power precisely for the complementary metal oxide semiconductor (CMOS) VLSI circuits, without requiring any knowledge on circuit structure and interconnections. The ANFIS to power estimation application is relatively new. Power estimation using ANFIS is carried out by creating initial FIS modes using hybrid optimisation and back-propagation (BP) techniques employing constant and linear methods. It is inferred that ANFIS with the hybrid optimisation technique employing the linear method produces better results in terms of testing error that varies from 0% to 0.86% when compared to BPNN as it takes the initial fuzzy model and tunes it by means of a hybrid technique combining gradient descent BP and mean least-squares optimisation algorithms. ANFIS is the best suited for power estimation application with a low RMSE of 0.0002075 and a high coefficient of determination (R) of 0.99961.     

一种低复杂度Turbo乘积码自适应Chase译码算法

针对Turbo乘积码(Turbo Product Codes, TPCs)中的译码问题,该文提出一种全新的低复杂度TPC自适应Chase迭代译码算法。与已有的报道不同,在译码过程中,新算法首先统计TPC码块内每一行(列)产生的代数译码后的备选序列与接收序列的相同最小欧氏距离的个数,然后根据统计结果,按照算法步骤调整译码所需的不可靠位数值。通过Monte Carlo仿真可验证,当TPC行列编码采用相同的扩展汉明码,且编码效率为0.879时,该算法与Pyndiah采用固定不可靠位数值迭代译码算法相比,在误码率BER为10-4处仅损失约0.08 dB的性能,但是译码平均复杂度降低可达到约40.4%。     

数字滤波器的近似等波纹设计法

本文证明了数字滤波器的自适应设计法等价于MMSE设计法，并提出用幅度误差函数对代价函数进行自适应迭代的算法达到近似等滤纹数字滤波器的自适应设计法．实验证明该设计方法简单有效，适合用于滤波器的工程设计。     

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.