System Maintenance:
There may be intermittent impact on performance while updates are in progress. We apologize for the inconvenience.
By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Yunfei Xu ; Dept. of Mech. Eng., Michigan State Univ., East Lansing, MI, USA ; Jongeun Choi ; Dass, S. ; Maiti, T.

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.

Published in:

Decision and Control (CDC), 2012 IEEE 51st Annual Conference on

Date of Conference:

10-13 Dec. 2012