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 language||English (US)|
|Title of host publication||25th European Signal Processing Conference, EUSIPCO 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Oct 23 2017|
|Event||25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece|
Duration: Aug 28 2017 → Sep 2 2017
|Name||25th European Signal Processing Conference, EUSIPCO 2017|
|Other||25th European Signal Processing Conference, EUSIPCO 2017|
|Period||8/28/17 → 9/2/17|
Bibliographical noteFunding 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.
© EURASIP 2017.
- Graph signal reconstruction
- Kriged Kalman filtering
- Laplacian kernels
- Time series on graphs