Sensor placement for spatial Gaussian processes with integral observations
| Autor: | Krista Elena Longi, Chang Rajani, Tom Oskar Nikolai Sillanpää, Joni Mikko Kristian Mäkinen, Timo Rauhala, Ari Salmi, Edward Haeggström, Arto Olavi Klami |
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| Přispěvatelé: | Department of Computer Science, Multi-source probabilistic inference research group / Arto Klami, Department of Physics, Materials Physics, Helsinki Institute for Information Technology |
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| Zdroj: | Scopus-Elsevier University of Helsinki |
| Popis: | Gaussian processes (GP) are a natural tool for estimating unknown functions, typically based on a collection of point-wise observations. Interestingly, the GP formalism can be used also with observations that are integrals of the unknown function along some known trajectories, which makes GPs a promising technique for inverse problems in a wide range of physical sensing problems. However, in many real world applications collecting data is laborious and time consuming. We provide tools for optimizing sensor locations for GPs using integral observations, extending both model-based and geometric strategies for GP sensor placement. We demonstrate the techniques in ultrasonic detection of fouling in closed pipes. |
| Databáze: | OpenAIRE |
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