传感器网络一致性分布式滤波算法

Consensus-based distributed filtering algorithm in sensor networks

为了改善分布式传感器网络的估计性能,提出了一种基于状态预测一致的滤波算法.在对局部估计值进行一致化处理的基础上,重点研究了利用邻居节点前一时刻的估计值对当前局部状态预测值进行修正来提高估计精度.给出了一种一致性增益的选择方法,利用李雅普诺夫方法得到了算法收敛的充分条件,并讨论了影响算法收敛速度的因素.仿真结果表明了算法的有效性,并发现节点度较大的传感器在网络估计中发挥着重要作用,可通过调整这类节点的一致性系数来改善算法性能.

To improve the estimation performances in distributed sensor networks,we propose a filtering algorithm based on state prediction consensus.On the basis of the consistent treatment of the local estimate,it updates the local predicted value by using the previous estimates of neighbors to improve the estimation accuracy.We propose a method for choosing the consensus gain,and present,by using the Lyapunov method,a sufficient condition for the convergence of the algorithm.Factors which may affect the convergence rate are discussed.A numerical example is given to illustrate the usefulness of the proposed algorithm.Nodes with high degrees play an important role in the filtering.By adjusting consensus coefficients of these nodes,we can improve estimation performances.