Mobile sensor networks for learning anisotropic gaussian processes

Yunfei Xu, Jongeun Choi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Citations (Scopus)


This paper presents a novel class of self-organizing sensing agents that learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of Gaussian processes developed to model a broad range of anisotropic, spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum likelihood (ML) estimator. The prediction of the field of interest is then obtained based on a non-parametric approach. An optimal navigation strategy is proposed to minimize the Cramér-Rao lower bound (CRLB) of the estimation error covariance matrix. Simulation results demonstrate the effectiveness of the proposed scheme.

Original languageEnglish
Title of host publication2009 American Control Conference, ACC 2009
Number of pages6
Publication statusPublished - 2009
Event2009 American Control Conference, ACC 2009 - St. Louis, MO, United States
Duration: 2009 Jun 102009 Jun 12

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619


Other2009 American Control Conference, ACC 2009
Country/TerritoryUnited States
CitySt. Louis, MO

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering


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