文献类型：期刊文献

英文题名：Efficient implementation of distributed maximum likelihood estimation method in clustered wireless sensor networks

作者：Zhang, X.F.[1]; Liu, H.Y.[2]

第一作者：张雪芬

机构：[1] College of Information Technology, Beijing Union University, 100101 Beijing, China; [2] Institute of Microelectronics of Chinese Academy of Sciences, 100029, Beijing, China

第一机构：北京联合大学智慧城市学院

年份：2013

卷号：157

期号：10

起止页码：101-107

外文期刊名：Sensors and Transducers

收录：EI(收录号：20140917410636);Scopus(收录号：2-s2.0-84894410357)

语种：英文

外文关键词：Conjugate gradient method - Estimation - Maximum likelihood estimation

摘要：The problem of efficient implementation of maximum-likelihood (ML) estimation of an unknown deterministic vector parameter in a clustered Wireless Sensor Network (WSN) is considered in this paper. In our previous work, we developed a distributed estimation manner through the combination of Lagrangian multiplier and block coordinate descent methods. In this paper, we give rigorous proof that the distributed estimation approach converges to the ML estimation result assuming ideal communication links. We also perform simulations to demonstrate the performance of the approach under additive-white Gaussian noise channel. Then we propose the efficient implementation of the most complicated step of the approach based on the preconditioned conjugate gradient method. Moreover, we present a hierarchical architecture for the implementation of the approach, which suggest our distributed estimation approach is favorable for practical uses. ? 2013 IFSA.