Observing and tracking bandlimited graph processes from sampled measurements

Isufi, E.; Banelli, Paolo; Di Lorenzo, Paolo; Leus, G.J.T.

doi:10.1016/j.sigpro.2020.107749

Observing and tracking bandlimited graph processes from sampled measurements

Title

Observing and tracking bandlimited graph processes from sampled measurements

Author

Isufi, E. (TU Delft Multimedia Computing)
Banelli, Paolo (University of Perugia)
Di Lorenzo, Paolo (Sapienza University of Rome)
Leus, G.J.T. (TU Delft Signal Processing Systems)

Date

2020

Abstract

A critical challenge in graph signal processing is the sampling of bandlimited graph signals; signals that are sparse in a well-defined graph Fourier domain. Current works focused on sampling time-invariant graph signals and ignored their temporal evolution. However, time can bring new insights on sampling since sensor, biological, and financial network signals are correlated in both domains. Hence, in this work, we develop a sampling theory for time varying graph signals, named graph processes, to observe and track a process described by a linear state-space model. We provide a mathematical analysis to highlight the role of the graph, process bandwidth, and sample locations. We also propose sampling strategies that exploit the coupling between the topology and the corresponding process. Numerical experiments corroborate our theory and show the proposed methods trade well the number of samples with accuracy.

Subject

Graph processes
Graph signal processing
Kalman filtering
Observability
Sampling on graphs
Time varying graph signals

To reference this document use:

http://resolver.tudelft.nl/uuid:5d966e85-70eb-4971-b8b4-047debe127aa

DOI

https://doi.org/10.1016/j.sigpro.2020.107749

ISSN

0165-1684

Source

Signal Processing, 177, 1-13

Part of collection

Institutional Repository