基于箕舌线函数的变步长归一化最小均方算法

对变步长归一化最小均方(VS-NLMS)自适应算法进行了讨论,针对其在自适应过程渐进稳态时对噪声干扰过于敏感的不足做了改进。同时,为了协调其低稳态误差与快速跟踪性能间的矛盾,引入基于相关误差项的变步长调整方案,同时采取了替代Sigmoid函数的箕舌线函数作为步长迭代公式,大大降低了计算复杂度。仿真结果表明,改进后的算法不仅具备优于归一化最小均方算法的收敛性能,同时具备了更小的稳态失调和快速灵敏的时变跟踪能力。     

最大互相关熵多凸组合自适应滤波算法

基于最大互相关熵准则(MCC)的自适应滤波算法在非高斯噪声环境下具有强鲁棒性,得到了广泛应用。然而,传统MCC滤波算法在选择参数时依然受到收敛速度与稳态精度之间固有矛盾的困扰。为解决这一问题,该文提出一类多凸组合MCC算法,能够充分发挥不同参数组合下滤波算法的性能优势,从而获得更好的信道跟踪能力。理论分析得出了所提算法的均值收敛条件和稳态均方误差,同时,仿真实验表明所提算法在对抗高斯和非高斯噪声时均具有收敛快、稳态精度高的特点。     

最大互相关熵多凸组合自适应滤波算法

基于最大互相关熵准则(MCC)的自适应滤波算法在非高斯噪声环境下具有强鲁棒性,得到了广泛应用.然而,传统MCC滤波算法在选择参数时依然受到收敛速度与稳态精度之间固有矛盾的困扰.为解决这一问题,该文提出一类多凸组合MCC算法,能够充分发挥不同参数组合下滤波算法的性能优势,从而获得更好的信道跟踪能力.理论分析得出了所提算法的均值收敛条件和稳态均方误差,同时,仿真实验表明所提算法在对抗高斯和非高斯噪声时均具有收敛快、稳态精度高的特点.     

一种变步长凸组合LMS自适应滤波算法改进及分析

为了避免单个滤波器在收敛速度与稳态误差上相互制约,从而导致系统性能降低的问题,本文采用凸组合最小均方算法（Combined Least Mean Square ,CLMS ）,将快速滤波器和慢速滤波器并联使用,同时为进一步改善CLMS算法的性能,对已有的变步长凸组合最小均方算法（Variable Step-size Convex Combination of LMS ,VSCLMS ）做出改进,提出了一种新的VSCLMS算法。在该算法中,对快速滤波器选用以最小均方权值偏差（Minimization of Mean Square Weight Error ,MMSWE）为准则的按步分析的变步长滤波器;对慢速滤波器采用以稳态最小均方误差（Least Mean Square , LMS ）为准则的固定步长滤波器。通过理论分析与仿真实验表明,该算法能够在噪声、时变以及非平稳的环境下保持较好的随动性能,且在各个阶段均保持良好的收敛性,与传统的CLMS、VSCLMS算法相比,不仅具有更快的收敛速度,而且拥有稳定的均方性能和较优的跟踪性能,为自适应滤波算法的研究提供了一条可行途径。     

零极值目标函数系统辨识算法

该文提出一种零极值目标函数最小化系统辨识算法。目标函数为系统均方误差与系统噪声方差之差的平方,其极小值为零。在系统辨识过程中采用滑动平均法在线估计系统均方误差、输入自相关矩阵以及输入与期望响应的互相关向量。推导出自适应滤波器权值向量的更新表达式。算法的步长能够根据统计量自适应地调整,使得在得到较小稳态误差的同时提高算法收敛速度。分析了算法的稳定性,得到了算法收敛的条件。对比实验结果表明,该算法具有更快的收敛速度,更小的稳态误差以及更好的稳定性。     

平方根变步长lp范数LMS算法的稀疏系统辨识

针对稀疏未知系统的辨识问题,提出了一种基于lp(0相似文献   

改进的LMS自适应噪声对消算法的应用

针对工业噪声的特点,将自适应对消算法应用到工业噪声的处理中。根据传统最小均方(Least Mean Square,LMS)自适应算法的缺点,文中通过构造合适的步长因子,引入参数使得算法在提高收敛速度的同时保证较小的稳态误差。放宽算法的约束性条件,以提高步长调整的精度。实验验证,提出的算法与其他算法相比,具有更快的收敛速度、更小的稳态误差以及优良的抗干扰性能。     

一种变步长LMS波束形成算法性能研究

智能天线的自适应算法通过迭代运算获取波束形成的最优权值矢量时,收敛速度和稳态误差是衡量一个算法是否优良的关键因素。它们的好坏直接影响着系统波束形成的性能。系统地分析了传统的最小均方（LMs）算法的收敛速度以及稳态误差的性能,在此基础上提出了一种新的变步长LMS算法,将此算法应用于波束形成,并用Matlab软件进行仿真。仿真结果表明,改进后的算法较传统LMS具有较快的收敛速度和较小的稳态误差。     

LMS自适应波束形成方法研究

研究了最小均方误差（LMS）和归一化最小均方误差（NLMS）自适应波束形成方法的性能,分析了影响波束形成性能的因素,通过计算机仿真实验验证了搜索步长、迭代次数、快拍对波束形成性能的影响,并比较了两种方法的收敛速度、稳态误差和抗干扰性能。     

一种空时CDMA系统的多用户检测算法

多用户检测技术在实际应用中,难以解决的问题是在减少计算复杂度的同时又提高系统性能。本文针对空时CDMA系统设计了一种多用户检测算法,即附带噪声梯度的最速下降算法。此算法利用变步长迭代的思想实现多用户检测,避免了最小均方误差(MMSE)多用户检测庞大而复杂的求逆运算和固定步长收敛速度较慢的问题。计算机仿真结果表明,在空时CDMA系统中使用本文算法,收敛速度大大增加,同时在保持系统性能的前提下能够容纳较多的用户。     

加权部分更新仿射投影算法

从数据重用因子出发,得到了基于部分更新仿射投影算法（SR-APA）的改进算法。该算法通过加权改变了SR-APA的数据筛选规律,从而降低了等效数据重用因子,并且通过对未加权原始数据的重新利用巧妙地避免了加权带来的条件数增加问题,最终达到了降低稳态均方误差（MSE）的效果。仿真结果表明,该算法不仅MSE比SR-APA低,收敛速度也比SR-APA快。在收敛速度相同时,该算法计算量只有SR-APA计算量的50%左右。     

一种改进的变步长ELMS算法

在简单讨论基本最小均方(LMS)算法的基础上,引入了扩展的最小均方(ELMS)算法,并分析说明了该算法能达到更小的稳态MSE。改进的变步长ELMS算法是在对有用信号的预测中采用了自适应为归一化的的最小均方(NLMS)预测估计器,步长的迭代中引入遗忘因子i,利用其与误差信号的加权和来产生新的步长参与迭代。理论分析与计算机仿真结果表明,该算法有较好的收敛性能和较小的稳态失调。     

基于变步长等变化自适应盲源分离算法

对于基于梯度自适应的盲源分离算法，认真选择步长参数以达到好的分离性能是非常必要的。如果为加快收敛速度而增大步长因子，将会导致大的稳态误差，甚至引起算法发散，因此固定步长因子无法解决收敛速度和稳态误差之间的矛盾。本文为EASI算法提出了一种变步长的解决方案。通过建立步长因子与分离矩阵相互差异之间的非线性关系，加快了收敛速度，减小了失调误差。计算机仿真结果与理论分析相一致，证实了该算法明显优于传统的EASI算法。     

二阶自适应陷波器频率估计方法统计性能分析

为对如何提高自适应陷波器频率估计精度提供参考,通过评估自适应陷波器频率估计方法性能,对基于均方误差函数的自适应陷波器频率估计方法进行了统计性能分析。首先,根据误差函数的不同,将自适应陷波器划分为自适应FIR陷波器和自适应IIR陷波器。然后,将自适应FIR陷波器看作自适应IIR陷波器的特例,重点分析了自适应陷波器的误差函数及稳态下的频率估计统计性能,讨论了自适应陷波器参数对正弦信号频率估计精度和收敛速度的影响。最后,给出正弦信号的频率估计计算结果。结果表明,实际计算结果同理论计算结果一致,证明了统计性能分析的正确性。      

Statistical performance analysis of a fast stochastic gradient for constrained adaptive time delay estimation

An efficient bi-state stochastic gradient is proposed for spontaneous constrained time delay estimation. The quantized stochastic gradient is an approximation of the polarity of the instantaneous delay estimation error. It is adjusted in such a way that it has a much higher probability to move in the correct direction at each iteration so as to enable a speed-up in the delay estimate to converge to global minimum in steady state. The performance of the delay estimator is evaluated statistically and an analytical solution for its convergence behavior is established. It is demonstrated that the proposed algorithm has at least a two-fold improvement in convergence speed when compared with the conventional approach, and this is verified by extensive simulation results.     

自适应陷波滤波器频率估计新方法及性能分析

现有的自适应陷波滤波器（ANF）受误差函数所限,导致其自适应频率估计方法收敛速度较慢,对初始迭代频率值设定范围要求较高,特别针对频率接近于0或π的信号,还存在频率估计精度不高、算法稳定性差的问题,为此,提出一种ANF频率估计新方法.首先,分析现有ANF方法估计信号频率时存在精度低、速度慢、稳定性差的原因,提出一种新误差函数以提升ANF收敛速度;然后,根据ANF估计信号频率时偏差产生的机理,通过偏差补偿方式,降低噪声对ANF的影响,以获得近似无偏的频率估计结果,提高ANF频率估计精度,同时与离散卡尔曼滤波相结合,以改善算法的稳定性,并对该方法进行稳态条件下的性能分析;最后,给出了ANF频率估计结果,并讨论了ANF各参数对频率估计精度的影响,给出了具体计算结果.计算表明本文方法的有效性与正确性.     

Gamma random waypoint mobility model for wireless ad hoc networks

Random waypoint (RWP) mobility model is widely used in ad hoc network simulation. The model suffers from speed decay as the simulation progresses and may not reach the steady state in terms of instantaneous average node speed. Furthermore, the convergence of the average speed to its steady state value is delayed. This usually leads to inaccurate results in protocol validation of mobile ad hoc networks modeling. Moreover, the probability distributions of speed vary over the simulation time, such that the node speed distribution at the initial state is different from the corresponding distribution at the end of the simulation. In order to overcome these problems, this paper proposes a modified RWP mobility model with a more precise distribution of the nodes' speed. In the modified model, the speeds of nodes are sampled from gamma distribution. The results obtained from both analysis and simulation experiments of the average speed and the density of nodes' speed indicate that the proposed gamma random waypoint mobility model outperforms the existing RWP mobility models. It is shown that a significant performance improvement in achieving higher steady state speed values that closely model the pre‐assumed average speeds are possible with the proposed model. Additionally, the model allows faster convergence to the steady state, and probability distribution of speed is steady over the simulation time. Copyright © 2012 John Wiley & Sons, Ltd.     

Error whitening criterion for adaptive filtering: theory and algorithms

Mean squared error (MSE) has been the dominant criterion in adaptive filter theory. A major drawback of the MSE criterion in linear filter adaptation is the parameter bias in the Wiener solution when the input data are contaminated with noise. We propose and analyze a new augmented MSE criterion called the Error Whitening Criterion (EWC). EWC is able to eliminate this bias when the noise is white. We will determine the analytical solution of the EWC, discuss some interesting properties, and develop stochastic gradient and other fast algorithms to calculate the EWC solution in an online fashion. The stochastic algorithms are locally computable and have structures and complexities similar to their MSE-based counterparts (LMS and NLMS). Convergence of the stochastic gradient algorithm is established with mild assumptions, and upper bounds on the step sizes are deduced for guaranteed convergence. We will briefly discuss an RLS-like Recursive Error Whitening (REW) algorithm and a minor components analysis (MCA) based EWC-total least squares (TLS) algorithm and further draw parallels between the REW algorithm and the Instrumental Variables (IV) method for system identification. Finally, we will demonstrate the noise-rejection capability of the EWC by comparing the performance with MSE criterion and TLS.     

一种基于改进的DSE-CMA算法的盲多用户检测

提出了一种基于改进抖动符号误差恒模算法（Modified Dithered Sign—Error Constant Modulus Algorithm,MDSE—CMA）的盲多用户检测算法。抖动符号误差恒模算法（Dithered Sign—Error Constant Modulus Algorithm,DSE—CMA）稳态性能好，但其过量的均方误差使得收敛速度较慢。MDSE—CMA算法通过引入符合正弦概率密度分布的抖动信号来减少均方误差。基于该算法的多用户检测方法与DSE—CMA算法相比，收敛速度加快。仿真证明，提出的方法在码分多址（Code Division Multiple Aceess，CDMA）异步多径信道下可有效地抑制多址干扰和克服多径衰落。     

New Adaptive All-pass Based Notch Filter for Narrowband/FM Anti-jamming GPS Receivers

The global positioning system (GPS), which provides accurate positioning and timing information, has become a commonly used navigation instrument for many applications. The application of a new adaptive all-pass based notch filter (ANFA) for narrowband/FM interference suppression and frequency estimation in GPS receivers is proposed. An ANFA structure that achieves better unbiased characteristics with its coefficients is employed to accurately estimate the narrowband interfering signals in online fashion. A variable convergence factor that optimizes the maximal mean square error (MSE) reduction in each iteration is applied in a modified adaptive Gaussian–Newton (MAGN) algorithm. The proposed MAGN algorithm can lead to both faster convergence speed and higher estimation accuracy. Simulation results show that the ANFA offers a better performance than conventional linear predictors in terms of the SNR improvement and the mean output power (MOP) under the interference environments of interest.