Inference of spatiotemporal processes over graphs via kernel kriged Kalman filtering

Vassilis N. Ioannidis, Daniel Romero, Georgios B Giannakis

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

5 Scopus citations

Abstract

Inference of space-time signals evolving over graphs emerges naturally in a number of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel-based kriged Kalman filtering approach that leverages the spatio-temporal dynamics to allow for efficient online reconstruction, while also coping with dynamically evolving network topologies. Laplacian kernels are employed to perform kriging over the graph when spatial second-order statistics are unknown, as is often the case. Numerical tests with synthetic and real data illustrate the superior reconstruction performance of the proposed approach.

Original languageEnglish (US)
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1679-1683
Number of pages5
ISBN (Electronic)9780992862671
DOIs
StatePublished - Oct 23 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: Aug 28 2017Sep 2 2017

Publication series

Name25th European Signal Processing Conference, EUSIPCO 2017
Volume2017-January

Other

Other25th European Signal Processing Conference, EUSIPCO 2017
CountryGreece
CityKos
Period8/28/179/2/17

Bibliographical note

Funding Information:
The work of V. N. Ioannidis and G. B. Giannakis was supported by ARO grant W911NF-15-1-0492 and NSF grants 1343248, 1442686, and 1514056.

Keywords

  • Graph signal reconstruction
  • Kriged Kalman filtering
  • Laplacian kernels
  • Time series on graphs

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