Filtering approaches to accelerated consensus in diffusion sensor networks
Filtering approaches to accelerated consensus in diffusion sensor networks
复制标题
扩散传感器网络中加速达成共识的过滤方法
DOI：
10.1002/dac.2540
复制
发表时间：
2013
影响因子：
2.1
通讯作者：
Abd-Elrady E
中科院分区：
文献类型：
--
作者：
Abd-Elrady E
分布式传感器网络的主要目标是对传感器获取的值达成一致或共识。解决这个问题的常用方法是使用每个传感器可以访问的那些值的迭代和加权线性组合。计算适当权重的不同方法已经被广泛研究，但最终的迭代算法仍然需要多次迭代才能提供对共识值的相当好的估计。本文研究了基于自适应和非自适应滤波技术的不同加速一致性方法，并将其应用于使用自适应投影次梯度法的声源定位问题。比较仿真研究表明，基于牛顿插值多项式和半定规划的非自适应多项式滤波器可以提供比使用约束仿射投影算法或随机梯度算法评估的自适应滤波器更快的一致性和更好的估计精度，前提是网络拓扑是已知的。版权所有© 2013约翰威利父子有限公司.
The main objective in distributed sensor networks is to reach agreement or consensus on values acquired by the sensors. A common methodology to approach this problem is using the iterative and weighted linear combination of those values to which each sensor has access. Different methods to compute appropriate weights have been extensively studied, but the resulting iterative algorithm still requires many iterations to provide a fairly good estimate of the consensus value. In this paper, different accelerating consensus approaches based on adaptive and non‐adaptive filtering techniques are studied and applied on the problem of acoustic source localization using the adaptive projected subgradient method. A comparative simulation study shows that the non‐adaptive polynomial filters based on Newton's interpolating polynomials and semi‐definite programming can provide more accelerated consensus and better estimation accuracy than adaptive filters evaluated using constrained affine projection algorithm or stochastic gradient algorithm provided that the network topology is known beforehand. Copyright © 2013 John Wiley & Sons, Ltd.
影响因子：
2.1
作者：
Tien-Wen Sung;Chu-Sing Yang
通讯作者：
Tien-Wen Sung;Chu-Sing Yang
影响因子：
3.9
作者：
E. Kokiopoulou;P. Frossard
通讯作者：
P. Frossard
使用自适应投影次梯度方法在扩散网络中学习
DOI：
--
发表时间：
2009
期刊：
IEEE International Conference on Acoustics, Speech, and Signal Processing
影响因子：
--
作者：
R. Cavalcante;I. Yamada;B. Mulgrew
通讯作者：
B. Mulgrew