Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Distributed sampling of random fields with unknown covariance
R. Graham, J. Cortés
Proceedings of the American Control Conference, St. Louis, Missouri, 2009, pp. 4543-4548
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 neighboring agents and
compute directions of maximum descent of the
estimation uncertainty. The technical approach
builds on a novel iterative reformulation of the
sequential field estimation from Bayesian
statistics, and combines tools from distributed
linear iterations, nonlinear programming, and
spatial statistics.
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Mechanical and Aerospace Engineering,
University of California, San Diego
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