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Sensors 2011, 11(3), 3051-3066; doi:10.3390/s110303051

Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks

Received: 4 January 2011 / Revised: 25 February 2011 / Accepted: 27 February 2011 / Published: 9 March 2011
(This article belongs to the Special Issue Adaptive Sensing)
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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.
Keywords: mobile sensor networks; Gaussian processes; adaptive sampling mobile sensor networks; Gaussian processes; adaptive sampling
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Xu, Y.; Choi, J. Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks. Sensors 2011, 11, 3051-3066.

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