卡尔曼滤波器
快速卡尔曼滤波
递归最小平方滤波器
不变扩展卡尔曼滤波器
控制理论(社会学)
α-β滤光片
自适应滤波器
集合卡尔曼滤波器
扩展卡尔曼滤波器
背景(考古学)
计算机科学
算法
核自适应滤波器
滤波器(信号处理)
数学
滤波器设计
移动视界估计
人工智能
计算机视觉
古生物学
控制(管理)
生物
作者
Constantin Paleologu,Jacob Benesty,Silviu Ciochină
标识
DOI:10.1109/icassp.2013.6637714
摘要
In this paper, we study the time-domain Kalman filter in the context of echo cancellation. We explain the fundamental differences between the Kalman filter and the recursive least-squares (RLS) algorithm. Also, we show that the normalized least-mean-square (NLMS) algorithm has a clear relationship with the Kalman filter. Furthermore, a simplified Kalman filter is derived and by a judicious choice of its parameters, this algorithm behaves like a variable step-size adaptive filter. Simulation results indicate the good performance of the optimal and simplified Kalman filtering algorithms.
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