Journal
SENSORS
Volume 11, Issue 3, Pages 3051-3066Publisher
MDPI
DOI: 10.3390/s110303051
Keywords
mobile sensor networks; Gaussian processes; adaptive sampling
Funding
- National Science Foundation [CMMI-0846547]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [0846547] Funding Source: National Science Foundation
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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.
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