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基于最小均方误差算法的自适应滤波器在仿真源程序-based on the minimum mean square error of the adaptive filter algorithm for the simulation source
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学位论文；运动物体跟踪方法主要包括卡尔曼滤波，Mean-shift，Camshifi算法，粒子滤波器，Snake模型等；应用卡尔曼滤波方法设计了一套煤矿矿工出入自动监测系统；提出了一种新的基于高斯混合模型的颜色特征提取方法，该方法克服了现有的Camshift算法Continuousl y Adaptive eanshift中跟踪目标特征提取精确度低和计算复杂度高的缺陷-Dissertation moving object tracking methods include Kalman filt
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In almost all analyzes of the least mean-square (LMS)
finite impulse response (FIR) adaptive algorithm, it is assumed that
the length of the adaptive filter is equal to that of the unknown
system impulse response. However, in many practical sit
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有源自适应噪声的最常用算法，该算法是基于滤波器的输出信号与期望响应之间的误差的均方值为最小。-Active adaptive noise most commonly used algorithm, which is based on the error between the filter output signal and the desired response minimum mean-square value.
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提出了一种基于粒子滤波的自适应运动目标跟踪方法。均值漂移算法是一种最优梯度下降法，通过
迭代来搜索目标，从而实现对运动目标的跟踪。-Adaptive moving target tracking method based on particle filter. The mean-shift algorithm is an optimal gradient descent method, through an iterative search for the target in order t
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Adaptive Filter (Least Mean Square) algorithm performance verification in MATLAB tool.
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Adaptive Filtering Primer with MATLAB® clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic process
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介绍了噪声抵消的原理和从强噪声背景中自适应滤波提取有用信号的方法，并对最小均
方
(LMS, Least Mean Squares)
、归一化
LMS(NLMS, Normalized Least Mean Squares)
和递推最小二乘
(RLS, Recursive Least Squares)
三种基本自适应算法进行了对比研究。计算机模拟仿真结果表明，这
几种算法都能通过有效抑制各种干扰来提高强噪声背景中的信号检测特性。相比之下，
RLS
算法
具有良好的收敛性
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