Multimodal Data Fusion in Sensor Networks via Copula Processes

Jingting Liu, Ido Nevat, Pengfei Zhang, Gareth W. Peters

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

6 Citations (Scopus)

Abstract

We develop an efficient data fusion algorithm for field reconstruction of multiple physical phenomena, which exhibit multiple modalities each with complex dependence behavior. In particular, we design a novel spatial model where multiple latent processes are modelled as Multi-Output Gaussian Process. We encode a linear dependency structure through a specified covariance function in both space and between different modalities of the spatial processes monitored. To account for different data modalities, we model the spatial dependence between each process via Copula dependence structures [1], thus allowing to choose any marginal distribution or process (possibly different) for each of the physical phenomena. We formulate the field reconstruction problem and develop a low complexity algorithm to approximate the intractable predictive posterior distribution. We show that our model significantly outperforms the model which treats the different physical phenomena independently in terms of prediction meansquared-errors (MSE). This provides the motivation to use our model for multimodal data fusion.
Original languageEnglish
Title of host publication2017 IEEE Wireless Communications and Networking Conference (WCNC)
PublisherIEEE
ISBN (Electronic)9781509041831
DOIs
Publication statusPublished - 11 May 2017
Event2017 IEEE Wireless Communications and Networking Conference - San Francisco, CA, USA, San Francisco, United States
Duration: 19 Mar 201722 Mar 2017

Publication series

NameIEEE Wireless Communications and Networking Conference
PublisherIEEE
ISSN (Electronic)1558-2612

Conference

Conference2017 IEEE Wireless Communications and Networking Conference
Abbreviated titleWCNC 2017
Country/TerritoryUnited States
CitySan Francisco
Period19/03/1722/03/17

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