Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields

Fully Bayesian Prediction Algorithms for Mobile Robotic Sensors under Uncertain Localization Using Gaussian Markov Random Fields

In this paper, we present algorithms for predicting a spatio-temporal random field measured by mobile robotic sensors under uncertainties in localization and measurements. The spatio-temporal field of interest is modeled by a sum of a time-varying mean function and a Gaussian Markov random field (GM...

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Bibliographic Details
Main Authors: Mahdi Jadaliha, Jinho Jeong, Yunfei Xu, Jongeun Choi, Junghoon Kim
Format: Article
Language:English
Published: MDPI AG 2018-08-01
Series:Sensors
Subjects:
Gaussian markov random field
fully Bayesian
mobile sensor network
localization uncertainty
Online Access:http://www.mdpi.com/1424-8220/18/9/2866

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http://www.mdpi.com/1424-8220/18/9/2866

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