OBSERVING BANDLIMITED GRAPH PROCESSES FROM SUBSAMPLED MEASUREMENTS

Elvin Isufi; Paolo Banelli; Paolo Di Lorenzo; Geert Leus

doi:10.1109/acssc.2018.8645200

OBSERVING BANDLIMITED GRAPH PROCESSES FROM SUBSAMPLED MEASUREMENTS

Elvin Isufi, Paolo Banelli, Paolo Di Lorenzo, Geert Leus

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

This work merges tools from graph signal processing and linear systems theory to propose sampling strategies for observing the initial state of a process evolving over a graph. The proposed method is ratified by a mathematical analysis that provides insights on the role played by the different actors, such as the graph topology, the process bandwidth, and the sampling strategy. Moreover, conditions when the graph process is observable from a few samples and (sub)optimal sampling strategies that jointly exploit the nature of the graph structure and graph process are proposed. Finally, numerical tests are conducted to illustrate the benefits of the proposed approach.

Original language	English (US)
Title of host publication	2018 52nd Asilomar Conference on Signals, Systems, and Computers
Publisher	IEEE
Pages	734-741
Number of pages	8
ISBN (Print)	9781538692189
DOIs	https://doi.org/10.1109/acssc.2018.8645200
State	Published - 2018
Externally published	Yes

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10.1109/acssc.2018.8645200

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@inproceedings{aa87ab806061403fb17815b91bc4ba9d,

title = "OBSERVING BANDLIMITED GRAPH PROCESSES FROM SUBSAMPLED MEASUREMENTS",

abstract = "This work merges tools from graph signal processing and linear systems theory to propose sampling strategies for observing the initial state of a process evolving over a graph. The proposed method is ratified by a mathematical analysis that provides insights on the role played by the different actors, such as the graph topology, the process bandwidth, and the sampling strategy. Moreover, conditions when the graph process is observable from a few samples and (sub)optimal sampling strategies that jointly exploit the nature of the graph structure and graph process are proposed. Finally, numerical tests are conducted to illustrate the benefits of the proposed approach.",

author = "Elvin Isufi and Paolo Banelli and {Di Lorenzo}, Paolo and Geert Leus",

note = "KAUST Repository Item: Exported on 2022-06-24 Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700 Acknowledgements: This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2018",

doi = "10.1109/acssc.2018.8645200",

language = "English (US)",

isbn = "9781538692189",

pages = "734--741",

booktitle = "2018 52nd Asilomar Conference on Signals, Systems, and Computers",

publisher = "IEEE",

}

TY - GEN

T1 - OBSERVING BANDLIMITED GRAPH PROCESSES FROM SUBSAMPLED MEASUREMENTS

AU - Isufi, Elvin

AU - Banelli, Paolo

AU - Di Lorenzo, Paolo

AU - Leus, Geert

N1 - KAUST Repository Item: Exported on 2022-06-24 Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700 Acknowledgements: This work was supported by the KAUST-MIT-TUD consortium grant OSR-2015-Sensors-2700. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2018

Y1 - 2018

N2 - This work merges tools from graph signal processing and linear systems theory to propose sampling strategies for observing the initial state of a process evolving over a graph. The proposed method is ratified by a mathematical analysis that provides insights on the role played by the different actors, such as the graph topology, the process bandwidth, and the sampling strategy. Moreover, conditions when the graph process is observable from a few samples and (sub)optimal sampling strategies that jointly exploit the nature of the graph structure and graph process are proposed. Finally, numerical tests are conducted to illustrate the benefits of the proposed approach.

AB - This work merges tools from graph signal processing and linear systems theory to propose sampling strategies for observing the initial state of a process evolving over a graph. The proposed method is ratified by a mathematical analysis that provides insights on the role played by the different actors, such as the graph topology, the process bandwidth, and the sampling strategy. Moreover, conditions when the graph process is observable from a few samples and (sub)optimal sampling strategies that jointly exploit the nature of the graph structure and graph process are proposed. Finally, numerical tests are conducted to illustrate the benefits of the proposed approach.

UR - http://hdl.handle.net/10754/679307

UR - https://ieeexplore.ieee.org/document/8645200/

UR - http://www.scopus.com/inward/record.url?scp=85062958847&partnerID=8YFLogxK

U2 - 10.1109/acssc.2018.8645200

DO - 10.1109/acssc.2018.8645200

M3 - Conference contribution

SN - 9781538692189

SP - 734

EP - 741

BT - 2018 52nd Asilomar Conference on Signals, Systems, and Computers

PB - IEEE

ER -

OBSERVING BANDLIMITED GRAPH PROCESSES FROM SUBSAMPLED MEASUREMENTS

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