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Consensus in Ad Hoc WSNs With Noisy Links—Part II: Distributed Estimation and Smoothing of Random Signals
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BibTeX
@MISC{Schizas_consensusin,
author = {Ioannis D. Schizas and Student Member and Georgios B. Giannakis and Stergios I. Roumeliotis and Ro Ribeiro and Student Member},
title = {Consensus in Ad Hoc WSNs With Noisy Links—Part II: Distributed Estimation and Smoothing of Random Signals},
year = {}
}
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Abstract
Abstract—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. Index Terms—Distributed estimation, Kalman smoother, nonlinear optimization, wireless sensor networks (WSNs).
Keyphrases
noisy link part ii ad hoc wsns random signal ad hoc wireless sensor network linear minimum mean-square error quantization noise index term decentralized tracking application abstract distributed algorithm individual estimate lmmse estimator numerical example wireless sensor network dynamical process local estimate data model single-hop neighbor kalman smoother inter-sensor communication link optimal estimation d-lmmse one stationary random signal nonlinear optimization d-map estimator closed form desired estimator distributed operation centralized estimation
