A Novel Least Mean Squares Algorithm for tracking a Discrete-time fBm Process

A. Gupta, S. Joshi
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引用次数: 3

Abstract

This paper presents a novel variable step-size LMS (VSLMS) algorithm for tracking a discrete-time fractional Brownian motion that is inherently non-stationary. In the proposed work, one of the step-size values requires time-varying constraints for the algorithm to converge to the optimal weights whereas the constraints on the remaining step-size values are time-invariant in the decoupled weight vector space. It computes the step-size matrix by estimating the Hurst exponent required to characterize the statistical properties of the signal at the input of the adaptive filter. The experimental set-up of an adaptive channel equalizer is considered for equalization of these signals transmitted over stationary AWGN channel. The performance of the proposed variable step-size LMS algorithm is compared with the unsigned VSLMS algorithm and is observed to be better for the class of non-stationary signals considered
一种用于跟踪离散时间fBm过程的新颖最小均方算法
本文提出了一种新的变步长LMS (VSLMS)算法,用于跟踪固有非平稳的离散时间分数阶布朗运动。在所提出的工作中,其中一个步长值需要时变约束才能使算法收敛到最优权值,而在解耦的权向量空间中,其余步长值的约束是时不变的。它通过估计表征自适应滤波器输入处信号的统计特性所需的Hurst指数来计算步长矩阵。考虑了一种自适应信道均衡器的实验装置,用于在固定AWGN信道上传输这些信号的均衡。将所提出的变步长LMS算法的性能与无符号VSLMS算法进行了比较,并观察到对于考虑的非平稳信号类别而言,该算法的性能更好
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