Paper Info
Title
Distributed Estimation of Dynamic Fields over Multi-agent Networks.
Abstract
This work presents distributed algorithms for estimation of time-varying random fields over multi-agent/sensor networks. A network of sensors makes sparse and noisy local measurements of the dynamic field. Each sensor aims to obtain unbiased distributed estimates of the entire field with bounded mean-squared error (MSE) based on its own local observations and its neighbors' estimates. This work develops three novel distributed estimators: Pseudo-Innovations Kalman Filter (PIKF), Distributed Information Kalman Filter (DIKF) and Consensus+Innovations Kalman Filter (CIKF). We design the gain matrices such that the estimators achieve unbiased estimates with bounded MSE under minimal assumptions on the local observation and network communication models. This work establishes trade-offs between these three distributed estimators and demonstrates how they outperform existing solutions. We validate our results through extensive numerical evaluations.
Year
Venue
Field
2017
arXiv: Information Theory
Mathematical optimization,Random field,Network communication,Matrix (mathematics),Kalman filter,Distributed algorithm,Wireless sensor network,Mathematics,Estimator,Bounded function
DocType
Volume
Citations 
Journal
abs/1701.02710
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Subhro Das101.69
José M. F. Moura25137426.14