Statistical gas distribution modeling using kernel methods

Sahar Asadi, Matteo Reggente, Cyrill Stachniss, Christian Plagemann, Achim J. Lilienthal

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

17 Scopus citations

Abstract

Gas distribution models can provide comprehensive information about a large number of gas concentration measurements, highlighting, for example, areas of unusual gas accumulation. They can also help to locate gas sources and to plan where future measurements should be carried out. Current physical modeling methods, however, are computationally expensive and not applicable for real world scenarios with real-time and high resolution demands. This chapter reviews kernel methods that statistically model gas distribution. Gas measurements are treated as random variables, and the gas distribution is predicted at unseen locations either using a kernel density estimation or a kernel regression approach. The resulting statistical models do not make strong assumptions about the functional form of the gas distribution, such as the number or locations of gas sources, for example. The major focus of this chapter is on two-dimensional models that provide estimates for the means and predictive variances of the distribution. Furthermore, three extensions to the presented kernel density estimation algorithm are described, which allow to include wind information, to extend the model to three dimensions, and to reflect time-dependent changes of the random process that generates the gas distribution measurements. All methods are discussed based on experimental validation using real sensor data.

Original languageEnglish
Title of host publicationIntelligent Systems for Machine Olfaction
Subtitle of host publicationTools and Methodologies
PublisherIGI Global
Pages153-179
Number of pages27
ISBN (Print)9781615209156
DOIs
StatePublished - 2011
Externally publishedYes

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