最小均方误差检测器对扩频系统中窄带干扰的抑制性能分析

本文首先说明当码元间隔内的码片数目与扩频序列周期相等时,直接序列扩频(DSSS)系统中最小均方误差(MMSE)意义下的最优滤波器也是最小误码率意义下的最优滤波器,因而能很好地抑制大多数干扰样式。本文具体分析了最小均方误差检测器对音频干扰以及自回归干扰的抑制性能,给出了详细的推导过程以及结论。     

基于LMS改进算法的UWB-SAR的RFI抑制方法

提出最小均方(LMS)改进算法,将LMS自适应滤波与SAR的RD处理算法相结合,并通过仿真验证了算法对射频干扰抑制的有效性。     

一种抗冲击噪声的对数总体最小二乘自适应滤波算法

在未知系统输入信号和输出信号均含有噪声的环境中,传统的自适应滤波算法,如最小均方(LMS)算法,会产生有偏估计.总体最小二乘(TLS)算法能够同时最小化输入信号与输出信号的噪声干扰,是解决此类问题的重要方法.然而,在许多实际应用中,干扰噪声可能具有冲击特性,这使得传统基于2阶统计量的自适应滤波算法,包括总体最小二乘算法...     

一种抗冲击噪声的对数总体最小二乘自适应滤波算法

在未知系统输入信号和输出信号均含有噪声的环境中,传统的自适应滤波算法,如最小均方(LMS)算法,会产生有偏估计.总体最小二乘(TLS)算法能够同时最小化输入信号与输出信号的噪声干扰,是解决此类问题的重要方法.然而,在许多实际应用中,干扰噪声可能具有冲击特性,这使得传统基于2阶统计量的自适应滤波算法,包括总体最小二乘算法性能严重恶化,以至于不能正常工作.为了解决这个问题,该文在总体最小二乘法的基础上,利用对数函数对其改进,提出了一种能够抗冲击干扰的对数总体最小二乘(L-TLS)算法.最后,通过计算机仿真实验验证了该新算法的有效性.     

一种基于LMS滤波的OFDM系统信道估计方法

提出了一种适用于OFDM系统的最小均方(LMS)滤波的信道估计算法,对发送序列中导频位置的信道响应进行LMS滤波,进一步得出所有子载波上的信道响应。仿真结果表明,该方法同基于离散傅里叶变换(DFT)的信道估计算法相比,改善了估计的均方误差(MSE)和误码率(BER)性能。     

DSSS通信中基于快速更新子带自适应滤波的窄带干扰抑制

本文面向直接序列扩频(DSSS)通信中的窄带干扰抑制,将分块更新子带自适应滤波的高频谱分隔特性和直接变换自适应滤波的逐点更新特性结合起来,提出了一种快速更新子带自适应(FRSAF)算法,给出了算法的迭代因子收敛界和快速实现结构。理论分析表明：该算法收敛迅速、迭代稳健,其性能明显优于经典子带自适应滤波算法和DCT/DFT-LMS算法,应用于DSSS通信可以得到优良的干扰抑制效果。仿真结果验证了上述结论。     

超宽带雷达射频干扰抑制研究

本文分析了目前已提出的一些超宽带雷达射频干扰抑制算法及其性能,结合到最小均方(LMS)自适应滤波在抗干扰中的广泛应用,研究了它在UWB雷达射频干扰抑制中的作用。仿真结果表明,LMS自适应滤波能很好地抑制射频干扰信号。     

基于双曲正切函数框架的最小均方算法

自适应滤波器在自适应控制、噪声消除、信道均衡、系统辨识以及生物医学等领域的应用中发挥着重要作用。由于其简单性、低计算量和易于实现等特点，其中最流行的自适应滤波算法是最小均方（Least Mean Square,LMS）算法。传统的LMS算法在处理高斯信号时具有良好的收敛性能，然而，针对非高斯信号的处理，自适应LMS算法的收敛性较差，甚至无法收敛。为了改进LMS算法在非高斯噪声干扰下的收敛性，本文通过将传统的LMS算法的代价函数嵌入到双曲正切（Hyperbolic Tangent）函数框架中设计了一种新的代价函数，从而提出了一种鲁棒的双曲正切最小均方（Hyperbolic Tangent Least Mean Square,HTLMS）算法。此外，针对HTLMS算法存在收敛速度与稳态误差相矛盾的问题，本文设计了一种可变λ参数的双曲正切最小均方（Variableλ-parameter Hyperbolic Tangent Least Mean Square,VHTLMS）算法。仿真结果表明，在系统辨识应用场景中，与LMS算法、最大相关熵准则（Generalized Maximum Corr...     

基于LMS算法的自适应滤波器仿真实现

为了达到最佳的滤波效果,使自适应滤波器在工作环境变化时自动调节其单位脉冲响应特性,提出了一种自适应算法:最小均方算法(LMS算法)。这种算法实现简单且对信号统计特性变化具有稳健性,所以获得了极为广泛的应用。针对用硬件实现LMS算法的自适应滤波器存在的诸多缺点,采用Matlab工具对基于LMS算法的自适应滤波器进行了仿真试验。仿真结果表明,应用LMS算法的自适应滤波器不仅可以实现对信号噪声的自适应滤除,还能用于系统识别。     

基于相控阵自适应零陷的大规模NGSO星座间干扰规避策略研究

针对大规模非静止轨道（Non-Geo Stationary Orbit,NGSO）通信星座系统间同频干扰的实时性和突发性问题,将自适应波束成形技术的变步长LMS(Least Mean Square,最小均方误差)算法应用于NGSO间干扰场景。分析自适应波束成形技术的适用场景,选取OneWeb星座系统与Starlink星座系统的馈线链路间下行干扰场景进行仿真。通过对现有算法进行系统收敛速度、稳健性的性能比较,当NLMS(Normalization Least Mean Square,归一化最小均方误差)算法的参数μ0=0.6时,算法具有较快的收敛速度以及较小的稳态误差,因此运算量小、易于星上硬件实现。分别利用固定步长LMS算法、ENLMS(Error Normalization Least Mean Square,误差归一化最小均方误差)算法以及μ0=0.6的NLMS算法计算最优权向量,并应用于干扰仿真场景。结果表明,μ0=0.6的NLMS算法最能有效规避NGSO通信星座系统间的同频干扰,ENLMS算法次之,固定LMS算法的干扰规避效果最差。     

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.     

扩频系统中最小误码率意义下的最优干扰抑制技术

通过论证最小化误码率(MBER),最小均方误差(MMSE)以及约束最小均值输出能量(MMOE)之间的关系,将MBER准则下最优干扰抑制器的设计转化为后两种准则下最优干扰抑制器的设计,并分别导出两种自适应算法:递推最小二乘(RLS)和盲递推最小二乘(BRLS).前者抑制干扰效果好,但需要期望信号;后者无需期望信号,但抑制效果较差.本文将两种算法合理配合,给出了动态环境下的干扰抑制方法.     

Stochastic analysis of the LMS algorithm with a saturationnonlinearity following the adaptive filter output

This paper presents a statistical analysis of the least mean square (LMS) algorithm with a zero-memory scaled error function nonlinearity following the adaptive filter output. This structure models saturation effects in active noise and active vibration control systems when the acoustic transducers are driven by large amplitude signals. The problem is first defined as a nonlinear signal estimation problem and the mean-square error (MSE) performance surface is studied. Analytical expressions are obtained for the optimum weight vector and the minimum achievable MSE as functions of the saturation. These results are useful for adaptive algorithm design and evaluation. The LMS algorithm behavior with saturation is analyzed for Gaussian inputs and slow adaptation. Deterministic nonlinear recursions are obtained for the time-varying mean weight and MSE behavior. Simplified results are derived for white inputs and small step sizes. Monte Carlo simulations display excellent agreement with the theoretical predictions, even for relatively large step sizes. The new analytical results accurately predict the effect of saturation on the LMS adaptive filter behavior     

应用Hopfield神经网络抑制高频地波雷达中短波通信干扰

首先分析混频输入级高频地波雷达中短波通信干扰的特征,借鉴用基于LMS算法的自适应滤波方法抑制短波通信干扰的处理思想,通过构建基于Hopfield神经网络的自适应滤波器来抑制短波通信干扰.通过仿真比较这两种自适应滤波器的处理效果,验证了用基于Hopfield神经网络的自适应滤波来抑制短波通信干扰方案是可行的.     

VLSI implementation of a new LMS-based algorithm for noise removal in ECG signal

Least mean square (LMS)-based adaptive filters are widely deployed for removing artefacts in electrocardiogram (ECG) due to less number of computations. But they posses high mean square error (MSE) under noisy environment. The transform domain variable step-size LMS algorithm reduces the MSE at the cost of computational complexity. In this paper, a variable step-size delayed LMS adaptive filter is used to remove the artefacts from the ECG signal for improved feature extraction. The dedicated digital Signal processors provide fast processing, but they are not flexible. By using field programmable gate arrays, the pipelined architectures can be used to enhance the system performance. The pipelined architecture can enhance the operation efficiency of the adaptive filter and save the power consumption. This technique provides high signal-to-noise ratio and low MSE with reduced computational complexity; hence, it is a useful method for monitoring patients with heart-related problem.     

Improvement in adaptive noise canceling using a nonlinear filter based on the pontryagin minimum principle

This paper introduces a new nonlinear filter that is used for adaptive noise canceling. The derivation and convergence properties of the filter are presented. The performance, as measured by the root mean square error between the signal and its estimate, is compared with that of the commonly used least mean square (LMS) algorithm. It is shown, through simulation, that the proposed nonlinear noise canceler has, on the average, better performance than the LMS canceler. The proposed adaptive noise canceler is based on the Pontryagin minimum principle and the method of invariant imbedding. The computational time for the proposed method is about 10% of that of the LMS, in the studied cases, which is a substantial improvement.     

Least mean p-power error criterion for adaptive FIR filter

An adaptive FIR filter based on the least mean p-power error (MPE) criterion is investigated. First, some useful properties of MPE function are studied. Three main results are as follows: 1) MPE function is a convex function of filter coefficients; so it has no local minima. 2) When input process and desired process are both Gaussian processes, then MPE function has the same optimum solution as the conventional Wiener solution for any p. 3) When input process and desired process are non-Gaussian processes, then MPE function may have better optimum solution than Wiener solution. Next, a least mean p-power (LMP) error adaptive algorithm is derived and some application examples are presented. Consequently, when the signal is corrupted by an impulsive noise, the adaptive algorithm with p=1 is preferred. Furthermore, when the signal is corrupted by noise or interference, the adaptive algorithm with proper choice of p may be preferred     

基于变换域全相位FIR自适应滤波算法

基于一种全相位FIR自适应滤波器,将重叠滤波思想引入变换域LMS算法,提出了DFT、DCT和DST变换域的带窗重叠自适应滤波算法(WO-TLMS).与传统的变换域LMS(TLMS)算法相比,WO-TLMS算法提高了收敛速度同时具有较低的稳态均方误差.理论分析了算法的收敛性,实验中通过和TLMS算法的比较验证了WO-TLMS算法的优越性.     

Combined LMS/F algorithm

A new adaptive filter algorithm has been developed that combines the benefits of the least mean square (LMS) and least mean fourth (LMF) methods. This algorithm, called LMS/F, outperforms the standard LMS algorithm judging either constant convergence rate or constant misadjustment. While LMF outperforms LMS for certain noise profiles, its stability cannot be guaranteed for known input signals even For very small step sizes. However, both LMS and LMS/F have good stability properties and LMS/F only adds a few more computations per iteration compared to LMS. Simulations of a non-stationary system identification problem demonstrate the performance benefits of the LMS/F algorithm