Distributed sampling of random fields with unknown covariance

Authors:
Rishi Graham;Jorge Cortés
Affiliations:
Department of Applied Mathematics and Statistics, University of California, Santa Cruz;Department of Mechanical and Aerospace Engineering, University of California, San Diego
Venue:
ACC'09 Proceedings of the 2009 conference on American Control Conference
Year:
2009

Citing 5
Cited 0

Parallel and Distributed Computation: Numerical Methods

Parallel and Distributed Computation: Numerical Methods
Monte Carlo Statistical Methods (Springer Texts in Statistics)

Monte Carlo Statistical Methods (Springer Texts in Statistics)
Optimal Design of Experiments (Classics in Applied Mathematics) (Classics in Applied Mathematics, 50)

Optimal Design of Experiments (Classics in Applied Mathematics) (Classics in Applied Mathematics, 50)
Estimation of Spatially Distributed Processes Using Mobile Spatially Distributed Sensor Network

SIAM Journal on Control and Optimization
Decentralized Environmental Modeling by Mobile Sensor Networks

IEEE Transactions on Robotics

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Abstract

This paper considers robotic sensor networks performing spatial estimation tasks. We model a physical process of interest as a spatiotemporal random field with mean unknown and covariance known up to a scaling parameter. We design a distributed coordination algorithm for an heterogeneous network composed of mobile agents that take point measurements of the field and static nodes that fuse the information received from the agents and compute directions of maximum descent of the estimation uncertainty. The technical approach builds on a novel reformulation of Bayesian sequential field estimation, and combines tools from distributed linear iterations, nonlinear programming, and spatial statistics.