基于新步长调整函数的正交小波变换自适应滤波方法

研究一种基于新的步长调整函数的正交小波变换最小均方自适应滤波算法,阐述了基于正交小波变换的自适应滤波原理,解释了正交小波变换能够提高算法收敛速度的原因。将一种新的步长调整函数应用于正交小波变换最小均方自适应滤波系统,通过模型识别检验了算法的收敛速度和稳态误差。使用该方法进行体震信号的自适应滤波,获得了更快的收敛速度和更好的滤波效果。     

基于子带阵列盲处理的空时干扰抑制

在卫星智能天线终端,传统空时自适应滤波处理中自适应算法需要信号信息而缺乏实时性,阵列处理算法复杂而抗干扰能力不足,针对此问题,提出了一种子带盲自适应阵列处理算法,用于直扩系统空时干扰抑制技术。子带阵列处理相对纯空域处理提高了阵列自由度,相对传统空时的抽头延迟线阵列自适应结构又大大降低了算法复杂度。提出的子带指数型变步长线性约束恒模算法的自适应阵列处理算法能在低算法复杂度下提供较高的收敛速度和收敛精度,不需要发送训练序列,可实现盲自适应波束形成,易于实现实时跟踪信号变化。仿真结果表明新的空时干扰抑制方案具有更好的抗干扰性能。     

一种成比例系数的子带自适应啸叫抑制算法

啸叫现象会严重影响扩声系统性能.采用自适应滤波算法辨识反馈路径的方式进行啸叫抑制,将成比例系数的无延时多带结构子带自适应滤波(Proportionate Delayless Multiband-structured Subband Adaptive Filtering,PDM-SAF)算法应用到啸叫抑制系统中.该算法继承了子带自适应滤波算法收敛速度快的优点,并考虑到反馈路径的稀疏特性和系统对实时性的要求,采用成比例系数的步长控制和无延时的误差计算方法.仿真结果表明,当扩声系统的反馈路径具有稀疏特性时,PDMSAF算法可以加快自适应滤波的收敛和跟踪速度.     

利用直接矩阵求逆和临界采样子带自适应滤波器的快速系统辨识

在许多应用中,子带自适应滤波器结构已经显示了其在计算和性能上的优点。基于最近提出的一个采用临界采样滤波器组的子带自适应结构,该文引入了子带直接矩阵求逆(DMI)算法。在保持了该算法快速收敛优点的同时,利用相关矩阵块三对角的特殊结构,降低了该算法的计算复杂度。理论分析及计算机实验显示,子带直接矩阵求逆算法只需经过较少的更新次数自适应子滤波器自由度的两倍,就能够收敛到高于最小均方误差的3dB附近。     

基于混沌神经网络的二维局部均值估计自适应滤波

为了提高自适应滤波的精度和收敛速度,提出了一种基于混沌神经网络的二维均值估计(LME)自适应滤波算法,在传统的二维LME自适应滤波方案中引入了混沌神经网络控制机制,用混沌神经网络自适应滤波器代替LME中的LMS自适应滤波算法,应用混沌神经网络估计局部期望输出进行滤波.仿真结果表明,该局部均值估计滤波器当输入信号为均值不为0且变化较大时,输出信号仍能较好地实现对输入信号的跟踪,获得了原始信号的主要特性,从均方误差曲面来看,算法具有较快的收敛速度和较高的滤波精度.     

基于滤波器组的MCAF噪声对消语音增强

在自适应噪声对消语音增强系统中,为了更好地加快自适应收敛速度,又不增加系统的计算复杂度,同时达到较好的增强效果,提出基于滤波器组的多通道自适应滤波(MCAF)语音增强;给出分析滤波器组与综合滤波器组的原型滤波器设计的具体方法.自适应滤波部分采用经典的LMS算法,同时结合多通道自适应滤波(MCAF),实现对含噪语音的处理,以达到增强效果.实验结果表明,相对于传统的子带LMS算法,基于滤波器组的多通道自适应滤波具有更好的性能,且加快了计算速度.     

一种改进的NLMS算法在声回波抵消中的应用

收敛速度和残余均方误差是衡量最小均方算法性能的重要指标。在声回波抵消算法中,为了寻求收敛速度快和计算量小的自适应算法,在归一化最小均方误差算法基础上,把当前时刻以前的误差引入归一化收敛因子中得到一种新算法,可以减小信号样本波动对权重带来的影响。该算法比传统的归一化最小均方算法收敛性能更好,稳态失调也比其小。计算机仿真结果表明,新算法在自适应回波抵消中的综合性能要优于传统的归一化最小均方误差算法。     

基于集员滤波的宽带有源噪声控制算法

子带结构是提高宽带噪声控制的有效方法,归一化子带自适应滤波(NSAF)结构消除了传统子带结构在输出端产生混叠分量的问题,但由于在每个子带上采用相同的全带自适应滤波器,使得计算量高于传统子带结构,集员滤波(SMF)技术具有数据选择更新的特点,可有效降低计算量,且在收敛速度和稳态均方误差之间具有较好的折中性。在此引入集员滤波技术,建立基于NSAF结构的无延迟前馈有源噪声控制系统,降低计算量,最后仿真验证了该算法对宽带噪声具有更优的降噪效果。     

一种多带非相干光子太赫兹通信系统中的SSBI抑制技术

光子太赫兹通信具有大带宽、低传输损耗等优势，故成为6G超高速无线通信的研究热点。由于极低的用户端复杂性和成本，基于非相干的包络检波太赫兹接收技术备受关注。提出了一种多带非相干光子太赫兹通信系统。通过多带自适应调制，实现高性能太赫兹点对多点覆盖传输；此外，通过光域自适应滤波，抑制非相干包络检波时多带之间的信号-信号拍频串扰，实现每个子带信号的低算法复杂度和低功耗接收。实验中，根据系统的传输响应，3个子带分别使用5.75 G Baud 64QAM、16QAM和4QAM的自适应调制方式；再通过自适应单边带光滤波，接收端仅使用最小均方均衡算法即成功实现了300 GHz频段多带太赫兹信号的2 m无线传输。     

基于自适应滤波的压缩感知重构方法

自适应滤波框架中,滤波器的抽头系数可以利用特定的自适应算法达到近似维纳解,从而使滤波器的输出误差达到最小.将这个框架应用到压缩感知重构信号中,信号的稀疏系数等效为滤波器系数权值向量,从而可获得最佳的稀疏系数,以高概率重构信号.本文介绍了已有学者研究出的一种L0最小均方算法(L0-LMS),该算法中引入零引力项加快了权矢量向稀疏解收敛的速度,保证解的稀疏性.通过仿真可知,基于自适应滤波算法重构稀疏信号的性能较好,甚至优于压缩感知中常用的OMP算法.     

Subband adaptive filtering with real-valued subband signals foracoustic echo cancellation

Adaptive filtering in subbands was originally proposed to overcome the limitations of conventional least-mean-square (LMS) algorithms. In general, subband adaptive filters offer computational savings, as well as faster convergence over the conventional LMS algorithm. However, improvements to current subband adaptive filters could be further enhanced by a more elegant choice of their design/structure. Classical subband adaptive filters employ DFT-based analysis and synthesis filter banks which results in subband signals that are complex-valued. The authors modify the structure of subband adaptive filters by using single-sideband (SSB) modulated analysis and synthesis filter banks, which result in subband signals that are real-valued. This simplifies the realisation of subband adaptive filters     

自适应QAM解调及频偏估计方法研究

提出了一种新型的基于自适应滤波的QAM解调算法和频偏估计方法.以最小均方误差自适应算法(LMS)为例,讨论了采用本解调算法解调的过程及其性能.本解调算法无需自适应滤波器完成收敛,从而降低了对采样频率和处理速度的要求.仿真结果表明:本解调算法的误码率理论值与仿真结果一致性好.同时,基于本解调算法的频偏估计,能方便地给出频偏大小.     

Architectural Synthesis of Computational Engines for Subband Adaptive Filtering

Architectural synthesis of low-power computational engines (hardware accelerators) for a subband-based adaptive filtering algorithm is presented. The full-band least mean square (LMS) adaptive filtering algorithm, widely used in various applications, is confronted by two problems, viz., slow convergence when the input correlation matrix is ill-conditioned, and increased computational complexity for applications involving use of large adaptive filter orders. Both of these problems can be overcome by the use of a subband-based normalized LMS (NLMS) adaptive filtering algorithm. Since this algorithm is not amenable to pipelining, delayed coefficient adaptation in the NLMS updation is used, which provides the required delays for pipelining. However, the convergence speed of this subband-based delayed NLMS (DNLMS) algorithm degrades with increase in the adaptation delay. We first present a pipelined subband DNLMS adaptive filtering architecture with minimal adaptation delay for any given sampling rate. The architecture is synthesized by using a number of function preserving transformations on the signal flow graph (SFG) representation of the subband DNLMS algorithm. With the use of carry-save arithmetic, the pipelined architecture can support high sampling rates limited only by the delay of two full adders and a 2-to-1 multiplexer. We then extend this synthesis methodology to synthesize a pipelined subband DNLMS architecture whose power dissipation meets a specified budget. This low-power architecture exploits the parallelism in the subband DNLMS algorithm to meet the required computational throughput. The architecture exhibits a novel tradeoff between algorithmic performance (convergence speed) and power dissipation. Finally, we incorporate configurability for filter order, sample period, power reduction factor, number of subbands and decimation/interpolation factor in the low-power architecture, thus resulting in a low-power subband computational engine for adaptive filtering.     

Adaptive algorithms for non-Gaussian noise environments: the orderstatistic least mean square algorithms

Convergence properties are studied for a class of gradient-based adaptive filters known as order statistic least mean square (OSLMS) algorithms. These algorithms apply an order statistic filtering operation to the gradient estimate of the standard least mean square (LMS) algorithm. The order statistic operation in OSLMS algorithms can reduce the variance of the gradient estimate (relative to LMS) when operating in non-Gaussian noise environments. A consequence is that in steady state, the excess mean square error can be reduced. It is shown that when the input signals are iid and symmetrically distributed, the coefficient estimates for the OSLMS algorithms converge on average to a small area around their optimal values. Simulations provide supporting evidence for algorithm convergence. As a measurement of performance, the mean squared coefficient error of OSLMS algorithms has been evaluated under a range of noise distributions and OS operators. Guidelines for selection of the OS operator are presented based on the expected noise environment     

一种新的变步长LMS自适应算法

研究了自适应最小均方误差滤波算法的步长选取问题。在分析现有变步长LMS算法的基础上,给出一种以双曲正切函数的改进形式为变步长的LMS算法。在相同收敛速度的前提下,该算法具有更小的超量均方误差;而在相同超量均方误差的前提下,该算法具有更快的收敛速度。经实验,仿真结果与理论分析相一致,证实了该算法的优越性。     

A New Normalized Subband Adaptive Filter Algorithm with Individual Variable Step Sizes

Proposed is a novel variable step size normalized subband adaptive filter algorithm, which assigns an individual step size for each subband by minimizing the mean square of the noise-free a posterior subband error. Furthermore, a noniterative shrinkage method is used to recover the noise-free priori subband error from the noisy subband error signal. Simulation results using the colored input signals have demonstrated that the proposed algorithm not only has better tracking capability than the existing subband adaptive filter algorithms, but also exhibits lower steady-state error.     

抗冲激干扰的稀疏惩罚约束遗漏最小均方算法

当被识别系统是稀疏系统时,传统的遗漏最小均方( LLMS )自适应算法收敛性能较差,特别在非高斯噪声环境中,该算法性能进一步恶化甚至算法不平稳收敛。为了解决因信道的稀疏性使算法收敛变慢的问题,对LLMS算法的代价函数分别利用加权詛1-norm和加权零吸引两种稀疏惩罚项进行改进;为了优化算法的抗冲激干扰的性能,利用符号函数对已改进的算法迭代式作进一步改进。同时,将提出的两个算法运用于非高斯噪声环境下的稀疏系统识别,仿真结果显示提出的算法性能优于现存的同类稀疏算法。     

Filter bank design for a subband adaptive filtering structure with critical sampling

Subband adaptive filtering structures are attractive in applications such as acoustic echo cancellation and channel equalization, due to their properties of decorrelating the input signal and reducing the computational complexity. Recently, a new adaptive filtering structure with critical sampling was proposed. In this paper, we describe an optimization procedure to select the analysis and synthesis filter banks of this new subband structure, so that minimum steady-state mean square error or fastest convergence rate can be achieved. Such filter-bank design method is based on a theoretical analysis of the convergence properties of the adaptation algorithm and uses a nonlinear optimization routine. Computer simulations illustrate the convergence improvements that can be obtained with the filter banks designed by the proposed method.     

A recursive least M-estimate algorithm for robust adaptive filtering in impulsive noise: fast algorithm and convergence performance analysis

This paper studies the problem of robust adaptive filtering in impulsive noise environment using a recursive least M-estimate algorithm (RLM). The RLM algorithm minimizes a robust M-estimator-based cost function instead of the conventional mean square error function (MSE). Previous work has showed that the RLM algorithm offers improved robustness to impulses over conventional recursive least squares (RLS) algorithm. In this paper, the mean and mean square convergence behaviors of the RLM algorithm under the contaminated Gaussian impulsive noise model is analyzed. A lattice structure-based fast RLM algorithm, called the Huber Prior Error Feedback-Least Squares Lattice (H-PEF-LSL) algorithm is derived. Part of the H-PEF-LSL algorithm was presented in ICASSP 2001. It has an order O(N) arithmetic complexity, where N is the length of the adaptive filter, and can be viewed as a fast implementation of the RLM algorithm based on the modified Huber M-estimate function and the conventional PEF-LSL adaptive filtering algorithm. Simulation results show that the transversal RLM and the H-PEF-LSL algorithms have better performance than the conventional RLS and other RLS-like robust adaptive algorithms tested when the desired and input signals are corrupted by impulsive noise. Furthermore, the theoretical and simulation results on the convergence behaviors agree very well with each other.     

基于子带分解的自适应滤波

本文对基于子带分解的自适应滤波做了研究，给出子带分解下的包含子带间滤波的最优维纳解和ＬＭＳ算法，并分析了其收敛性能和计算复杂度，与传统的ＬＭＳ算法相比，基于子带分解的自适应滤波具有更好的性能，计算机模拟结果也体现了这一点。