Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Cooperative adaptive sampling of random fields with partially
known covariance
R. Graham, J. Cortés
International Journal of Robust and Nonlinear Control 22 (5) (2012), 504-534
Abstract
This paper considers autonomous robotic sensor networks taking
measurements of a physical process for predictive purposes. The
physical process is modeled as a spatiotemporal random field. The
network objective is to take samples at locations that maximize the
information content of the data. The combination of
information-based optimization and distributed control presents
difficult technical challenges as standard measures of information
are not distributed in nature. Moreover, the lack of prior
knowledge on the statistical structure of the field can make the
problem arbitrarily difficult. Assuming the mean of the field is an
unknown linear combination of known functions and its covariance
structure is determined by a function known up to an unknown
parameter, we provide a novel distributed method for performing
sequential optimal design by a network comprised of static and
mobile devices. We characterize the correctness of the proposed
algorithm and examine in detail the time, communication, and space
complexities required for its implementation.
pdf | ps.gz
Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
Skype id:
jorgilliyo