title: Adaptive sampling for learning gaussian processes using mobile sensor networks.
abstract: This paper presents a novel class of self-organizing sensing agents that adaptively 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 introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP) estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme.
authors.affiliation: Department of Mechanical Engineering, Michigan State University, East Lansing, MI 48823, USA. xuyunfei@egr.msu.edu
authors.forename: Yunfei
authors.initials: Y
authors.lastname: Xu
authors.forename: Jongeun
authors.initials: J
authors.lastname: Choi
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003195:Computer Communication Networks / Q000295:instrumentation*; D003198:Computer Simulation; D004058:Diffusion; D009010:Monte Carlo Method; D016011:Normal Distribution; D058998:Remote Sensing Technology / Q000295:instrumentation*; D018709:Statistics, Nonparametric; D013997:Time Factors