Consensus in Ad Hoc WSNs With Noisy Links—Part II: Distributed Estimation and Smoothing of Random Signals
Consensus in Ad Hoc WSNs With Noisy Links—Part II: Distributed Estimation and Smoothing of Random Signals
复制标题
DOI：
10.1109/tsp.2007.908943
复制
发表时间：
2008-04
影响因子：
5.4
通讯作者：
I. Schizas;G. Giannakis;S. Roumeliotis;Alejandro Ribeiro
中科院分区：
文献类型：
--
作者：
I. Schizas;G. Giannakis;S. Roumeliotis;Alejandro Ribeiro
分布式算法开发的平稳随机信号的最佳估计和平滑（即使是非平稳）动态过程的基础上，一般相关的观察收集的ad hoc无线传感器网络（WSNs）。最大后验概率（MAP）和线性最小均方误差（LMMSE）计划，以及赞赏集中估计，示出可能重新制定分布式操作通过迭代（交替方向）的乘法器的方法。传感器与单跳邻居进行通信，他们的个人估计以及衡量本地估计距离共识有多远的乘数。当迭代达到共识时，得到的分布式（D）MAP和LMMSE估计器收敛到它们的集中式对应时，传感器间的通信链路是理想的。D-MAP估计器不需要所需的估计器以封闭形式表示，D-LMMSE估计器对通信或量化噪声具有可证明的鲁棒性，并且当数据模型是线性高斯时，两者都特别容易实现。对于分散跟踪应用，分布式卡尔曼滤波和平滑算法推导出任何时间MMSE最优共识为基础的状态估计使用无线传感器网络。分析和证实的数值例子证明了新的分布式估计的优点。
Distributed algorithms are developed for optimal estimation of stationary random signals and smoothing of (even nonstationary) dynamical processes based on generally correlated observations collected by ad hoc wireless sensor networks (WSNs). Maximum a posteriori (MAP) and linear minimum mean-square error (LMMSE) schemes, well appreciated for centralized estimation, are shown possible to reformulate for distributed operation through the iterative (alternating-direction) method of multipliers. Sensors communicate with single-hop neighbors their individual estimates as well as multipliers measuring how far local estimates are from consensus. When iterations reach consensus, the resultant distributed (D) MAP and LMMSE estimators converge to their centralized counterparts when inter-sensor communication links are ideal. The D-MAP estimators do not require the desired estimator to be expressible in closed form, the D-LMMSE ones are provably robust to communication or quantization noise and both are particularly simple to implement when the data model is linear-Gaussian. For decentralized tracking applications, distributed Kalman filtering and smoothing algorithms are derived for any-time MMSE optimal consensus-based state estimation using WSNs. Analysis and corroborating numerical examples demonstrate the merits of the novel distributed estimators.