Multimodal Data Fusion of Spatial Fields in Sensor Networks

Pengfei Zhang, Gareth W. Peters, Ido Nevat, Keng Boon Teo, Yixin Wang

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

14 Downloads (Pure)


We develop a robust data fusion algorithm for field reconstruction of multiple physical phenomena. The contribution of this paper is twofold: First, we demonstrate how multi-spatial fields which can have any marginal distributions and exhibit complex dependence structures can be constructed. Second, we develop an efficient and robust linear estimation algorithm to predict the mean behavior of the physical phenomena using rank correlation instead of the conventional linear Pearson correlation. Our approach has the advantage of avoiding the need to derive intractable predictive posterior distribution and also has a tractable solution for the rank correlation values. We show that our model outperforms the model which uses the conventional linear Pearson correlation metric in terms of the prediction mean-squared-errors (MSE). This provides the motivation for using our models for multimodal data fusion.
Original languageEnglish
Title of host publication2019 IEEE SENSORS
ISBN (Electronic)9781728116341
Publication statusPublished - 13 Jan 2020


Dive into the research topics of 'Multimodal Data Fusion of Spatial Fields in Sensor Networks'. Together they form a unique fingerprint.

Cite this