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.     

实时频域滤波干扰抑制补偿技术研究

针对直接序列扩频通信系统中实时频域滤波抑制干扰技术存在的不足,研究并仿真了信号时域加窗与重叠处理对实时频域滤波的改进,结果表明,经补偿的算法在一定程度上减小了频谱泄露,改善了通信系统接收信号与干扰噪声功率比,提高了系统的抗干扰性能。     

扩频系统频域窄带干扰抑制算法加窗损耗研究

扩频系统频域抗干扰算法通常采用数据加窗降低截断引起的频谱泄漏,采用数据重叠降低加窗带来的信噪比损耗。该文分析了数据加窗带来的信噪比损耗,研究了采用数据重叠后的信号合成输出问题,在Capozza(2000)的重叠选择输出方法的基础上提出了一种重叠相加输出方法。理论分析和仿真结果表明,与重叠选择输出方法相比,重叠相加输出方法减小了数据加窗引入的信噪比损耗,代价是增加了少量的加法运算。     

基于小波变换的变步长LMS自适应滤波算法

变步长LMS自适应滤波算法通过构造合适的步长因子有效的解决了传统LMS算法收敛速度和稳态误差相矛盾的问题.变换域LMS自适应滤波算法通过正交变换降低了输入信号矩阵的相关性,提高了算法的收敛速度.将这两种算法相结合,提出了一种新的基于小波变换的变步长LMS自适应滤波算法.仿真结果表明,该算法无论是收敛速度还是稳态误差都有了很大的提高.     

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。     

自适应滤波算法的仿真及工程实现

为了对自适应滤波算法的滤波性能进行分析,在自适应滤波理论研究的基础上,研究自适应滤波器结构及LMS自适应滤波算法.给出LMS算法的求解的公式,基于LMS算法求解公式,采用Matlab仿真和DSP软件编程两种方法实现了LMS算法,并给出了不同信噪比条件下,LMS算法的仿真实现的滤波结果及DSP实现的滤波结果,通过两种结果的比较可以看出,在信噪比较低的条件下,LMS算法工程上的滤波效果明显达不到理论上的滤波效果.该研究对于自适应滤波理论的工程应用,具有一定的指导作用.     

一种基于多尺度小波变换的自适应滤波新算法

将多尺度小波变换的理论引入到LMS自适应滤波器的设计中,分析了基于多尺度正交小波变换的自适应滤波算法的原理;将变步长LMS算法与多尺度小波变换的思想结合,提出了一种新的小波自适应滤波算法(MSWT-MVSS-LMS),新算法既减少了输入向量自相关矩阵条件数,又克服了固定步长LMS算法在收敛速度与收敛精度方面与步长因子μ的矛盾,获得了更好的收敛速度和稳定性.仿真结果表明新算法是有效的和优越的.     

自适应滤波算法研究及其Matlab实现

在对自适应滤波器相关理论研究的基础上,重点研究了LMS自适应滤波算法,给出了不同信噪比条件下,LMS算法的Matlab仿真实现的滤波结果,通过分析仿真结果可以看出,在一定信噪比范围内,LMS算法在未知信号与噪声统计特性的条件下可以达到较好的滤波效果.     

一种新的可变步长LMS自适应滤波算法及其算法分析

在讨论基本LMS．变步长NLMS和LMS／F组合自适应滤波算法的基础上提出一种新的可变步长LMS自适应滤波算法,新算法引入修正系数和遗忘因子．并利用和来产生新的步长参与迭代。计算机仿真结果表明,与基本LMS算法或变步长NLMS、LMS／F组合算法相比,新算法在保持算法简单这一特点的同时进一步加快了收敛速度,并能够收敛到更小且稳定的均方误差(MSE)。     

一种变步长LMS自适应滤波的改进算法

在信号处理领域,变步长LMS自适应滤波算法被广泛应用,其主要优点为具有较强的鲁棒性和简单易实现.但是,该算法在收敛速度上总是差强人意.针对这个问题对现有的变步长LMS自适应滤波算法进行了改进.首先,介绍了典型LMS算法和传统变步长LMS自适应滤波算法的基本原理及其局限性,在此基础上针对现有基于Sigmoid函数变步长最...     

Optimal tracking of time-varying channels: a frequency domainapproach for known and new algorithms

In this paper, we developed a systematic frequency domain approach to analyze adaptive tracking algorithms for fast time-varying channels. The analysis is performed with the help of two new concepts, a tracking filter and a tracking error filter, which are used to calculate the mean square identification error (MSIE). First, we analyze existing algorithms, the least mean squares (LMS) algorithm, the exponential windowed recursive least squares (EW-RLS) algorithm and the rectangular windowed recursive least squares (RW-RLS) algorithm. The equivalence of the three algorithms is demonstrated by employing the frequency domain method. A unified expression for the MSIE of all three algorithms is derived. Secondly, we use the frequency domain analysis method to develop an optimal windowed recursive least squares (OW-RLS) algorithm. We derive the expression for the MSIE of an arbitrary windowed RLS algorithm and optimize the window shape to minimize the MSIE. Compared with an exponential window having an optimized forgetting factor, an optimal window results in a significant improvement in the h MSIE. Thirdly, we propose two types of robust windows, the average robust window and the minimax robust window. The RLS algorithms designed with these windows have near-optimal performance, but do not require detailed statistics of the channel     

Frequency-domain motion estimation using a complex lapped transform

A frequency-domain algorithm for motion estimation based on overlapped transforms of the image data is developed as an alternative to block matching methods. The complex lapped transform (CLT) is first defined by extending the lapped orthogonal transform (LOT) to have complex basis functions. The CLT basis functions decay smoothly to zero at their end points, and overlap by 2:1 when a data sequence is transformed. A method for estimating cross-correlation functions in the CLT domain is developed. This forms the basis of a motion estimation algorithm that calculates vectors for overlapping, windowed regions of data. The overlapping data window used has no block edge discontinuities and results in smoother motion fields. Furthermore, when motion compensation is performed using similar overlapping regions, the algorithm gives comparable or smaller prediction errors than standard models using exhaustive search block matching, and computational load is lower for larger displacement ranges and block sizes.     

A Robust Least Mean Square Algorithm for Adaptive Array Signal Processing

Least Mean Square (LMS) has been the most popular scheme in the realization of adaptive beamforming algorithms. In this paper a Robust Least Mean Square (R-LMS) algorithm is proposed which uses ratio parameters to control the contribution of product vectors in the weight upgrading process. The idea behind the proposed scheme is inclusion of previous information in place of relying solely on current sample. The performance enhancement by R-LMS algorithm is achieved with insignificant increase in computational complexity of LMS algorithm, so the crux of the conventional technique is not lost. Simulation results are also presented which illustrate that R-LMS provides relatively fast convergence, less Brownian motion and improved stability.     

LMS自适应滤波器的设计理论研究

主要介绍了自适应滤波器的基本理论思想,具体阐述了自适应滤波器的基本原理、算法及设计方法。首先介绍自适应滤波器的原理,然后对FIR结构滤波器做了详细的阐述,自适应算法是整个系统的核心,重点对LMS算法的设计方法,设计步骤做了分析,最后对LMS算法进行MATLAB仿真,根据性能评价标准和实验结果表明,该自适应滤波器滤波效果优越。     

Adaptive parallel interference cancellation for CDMA systems—A weight selection and filtering scheme

Parallel interference cancellation (PIC) is a well-known multiuser detection algorithm in direct-sequence code-division multiple-access (DS-CDMA) systems. It is typically implemented with a multi-stage architecture. One problem associated with the PIC is that unreliable interference cancellation may occur in the early stages and the system performance may be degraded. Thus, the partial PIC detector was developed to control the cancellation level by use of interference cancellation factors. Partial PIC can be implemented with an adaptive form, in which optimal weights are derived using the least mean square (LMS) algorithm. In this paper, we propose an algorithm improving the conventional adaptive partial PIC. The main idea is to reduce the number of active weights in the LMS algorithm, and to perform weight post-filtering such that the resultant excess mean square error can be reduced. We also analyze the performance of the proposed algorithm and derive the bit error rate of the second stage output. Simulation results verify that the proposed algorithm outperforms the conventional partial PIC, and derived analytical results are accurate.     

LMS算法的二次稳定性及鲁棒LMS算法

本文在时域内研究LMS算法(least mean square algorithm)的稳定性及鲁棒LMS算法的构造.首先将LMS算法表达式转化为标准的离散时间系统状态方程形式,之后运用线性矩阵不等式(LMI)技术对其二次稳定性进行了分析.针对滤波过程中会出现的输入和测量噪声干扰,本文提出了一种兼顾收敛性、鲁棒稳定性以及鲁棒性能的鲁棒LMS算法,最后给出了仿真算例,通过和一般的LMS算法的比较,体现了这种鲁棒LMS算法的优越性.     

基于S形函数的变步长LMS算法

针对定步长LMS算法在收敛速度、时变跟踪能力和稳态失调噪声几个重要指标上不能兼顾的问题,引入人工神经网络中一种常用激励函数———S形函数,并将其应用于变步长LMS算法中。结合算法对收敛速度、精度及稳态失调噪声的要求,引入改变S形函数曲线曲率及收敛终值的2个参数：α和β。分析当α和β值固定时的情况,仿真结果表明定参数算法若其值选择不当会引起较大误差。按照步长值在时变阶段自适应增大,在稳态阶段步长很小的原则,构造了变参数α（n）和β（n）,仿真结果表明变参数算法兼顾多个参数,整体表现较好。     

Tracking of changes in latency and amplitude of the evokedpotential by using adaptive LMS filters and exponential averagers

The adaptive LMS algorithm in combination with exponential averagers are compared to the use of exponential averagers only in tracking latency and amplitude changes in the evoked potential. The estimator is intended for use in applications where neurologic functions are monitored by detecting changes in the evoked potential. Two different structures of the estimator are evaluated and it is found that averaging before filtering is to be preferred. It is shown that the desired signal to the LMS-filter can have a rather low SNR with only mirror influence on the estimator performance. The estimator which combines an LMS filter and an exponential averager was shown to detect changes in latency faster than the estimator which uses a nonfiltered average. The LMS filter is shown to exhibit bias in the estimate of the evoked potential due to the fact that response and background spectra has overlapping frequency ranges. The bias seems not to affect the latency estimation while amplitude estimation was clearly affected. Simulations are performed with both white noise and EEG background     

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。