A unified Bayesian approach for prediction and detection using mobile sensor networks

Yunfei Xu, Jongeun Choi, Sarat Dass, Tapabrata Maiti

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)


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
Article number6426817
Pages (from-to)1180-1185
Number of pages6
JournalProceedings of the IEEE Conference on Decision and Control
Publication statusPublished - 2012
Event51st IEEE Conference on Decision and Control, CDC 2012 - Maui, HI, United States
Duration: 2012 Dec 102012 Dec 13

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Control and Optimization


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