Extended Least Squares Identificationof Doubly Spread Mobile Communication Channels
Extended Least Squares Identificationof Doubly Spread Mobile Communication Channels
复制标题
双频移动通信信道的扩展最小二乘辨识
DOI：
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
R. Evans
中科院分区：
文献类型：
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作者：
L. Davis;I. Collings;R. Evans
本文对Tsatsanis等人的方法提出了一种在线耦合滤波方法。[1]用于识别随机时变线性信道。信道被建模为抽头延迟线滤波器，抽头系数由未知的自回归(AR)过程描述。我们提出了一种用于跟踪信道抽头和估计平均信道响应的增广状态卡尔曼滤波。对文献[1]的在线估计信道协方差和AR参数的递推算法进行了改进，加入了平均信道估计。这导致了耦合估计器结构，其中AR参数和平均信道响应被并行地估计。我们还注意到协方差估计技术与最大似然估计的关系。仿真研究证明了所提出的估计器的性能。
This paper presents an on-line coupled filter extension to the method of Tsatsanis et al. [1] for identification of randomly time-varying linear channels. The channel is modelled as a tapped-delay line filter, with tap coefficients described by an unknown auto-regressive (AR) process. We propose an augmented-state Kalman filter for tracking the channel taps and estimating the mean channel response. The recursive algorithm of [1] for estimating the channel covariance and AR parameters on-line is modified to incorporate the mean channel estimate. This results in a coupled estimator structure in which the AR parameters and mean channel response are estimated in parallel. We also note the relationship of the covariance estimation technique to maximum likelihood estimation. Simulation studies demonstrate the performance of the proposed estimator.