A Unified Bayesian Approach for Prediction and Detection Using Mobile Sensor Networks

Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we develop a unified Bayesian approach that enables the prediction of binary random events and random scalar fields from heterogeneous data collected by mobile sensor networks with different detectors and sensors. The heterogeneous uncertainties such as different false detection rates and measurement noises are taken into account. This proposed unified approach exploits the statistical correlations among heterogeneous random events and random fields via their latent random variables which are modeled by a Gaussian Markov random field. The statistical inference based on Gaussian approximation is then provided in order to predict the random events and/or scalar fields. The fully Bayesian approach based on the integrated nested Laplace approximation is also proposed to deal with the case where model parameters are not known a priori. Simulation results demonstrate the correctness and usefulness of the proposed approaches.
Original languageEnglish
Title of host publication2012 IEEE 51st IEEE Conference on Decision and Control (CDC)
PublisherIEEE
Pages1180-1185
Number of pages6
ISBN (Electronic)9781467320665
ISBN (Print)9781467320658
DOIs
Publication statusPublished - 2012

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