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A diffusion subband adaptive filtering algorithm for distributed estimation using variable step size and new combination method based on the MSD

机译:一种基于变步长的分布式子带自适应滤波分布式估计算法及基于MSD的新组合方法

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This paper proposes a novel diffusion subband adaptive filtering algorithm for distributed networks. To achieve a fast convergence rate and small steady-state errors, a variable step size and a new combination method is developed. For the adaptation step, the upper bound of the mean-square deviation (MSD) of the algorithm is derived and the step size is adaptive by minimizing it in order to attain the fastest convergence rate on every iteration. Furthermore, for a combination step realized by a convex combination of the neighbor-node estimates, the proposed algorithm uses the MSD, which contains information on the reliability of the estimates, to determine combination coefficients. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of the convergence rate and the steady-state errors. (C) 2015 Elsevier Inc. All rights reserved.
机译:提出了一种新颖的分布式子带扩散子带自适应滤波算法。为了实现快速收敛速度和较小的稳态误差,开发了可变步长和新的组合方法。对于自适应步骤,推导算法的均方差(MSD)的上限,并通过将步长最小化来自适应调整步长,以便在每次迭代中获得最快的收敛速度。此外,对于通过邻居节点估计的凸组合实现的组合步骤,所提出的算法使用MSD来确定组合系数,该MSD包含有关估计可靠性的信息。仿真结果表明,该算法在收敛速度和稳态误差方面均优于现有算法。 (C)2015 Elsevier Inc.保留所有权利。

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