Sensor Placement for Spatial Gaussian Processes with Integral Observations
Abstract
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.
Cite
Text
Longi et al. "Sensor Placement for Spatial Gaussian Processes with Integral Observations." Uncertainty in Artificial Intelligence, 2020.Markdown
[Longi et al. "Sensor Placement for Spatial Gaussian Processes with Integral Observations." Uncertainty in Artificial Intelligence, 2020.](https://mlanthology.org/uai/2020/longi2020uai-sensor/)BibTeX
@inproceedings{longi2020uai-sensor,
title = {{Sensor Placement for Spatial Gaussian Processes with Integral Observations}},
author = {Longi, Krista and Rajani, Chang and Sillanpää, Tom and Mäkinen, Joni and Rauhala, Timo and Salmi, Ari and Haeggström, Edward and Klami, Arto},
booktitle = {Uncertainty in Artificial Intelligence},
year = {2020},
pages = {1009-1018},
volume = {124},
url = {https://mlanthology.org/uai/2020/longi2020uai-sensor/}
}