TY - GEN
T1 - Distributed Analytical Graph Identification
AU - Chepuri, Sundeep Prabhakar
AU - Coutino, Mario
AU - Marques, Antonio G.
AU - Leus, Geert
N1 - KAUST Repository Item: Exported on 2022-06-30
Acknowledged KAUST grant number(s): OSR-2015-Sensors-2700
Acknowledgements: This work is supported in part by the KAUST-MIT-TUD consortium under grant OSR-2015-Sensors-2700, the ASPIRE project (project 14926 within the STW OTP program), and MINECO (grants TEC2013-41604-R and TEC2016-75361-R). Mario Coutino is partially supported by CONA-CYT.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2018/9/13
Y1 - 2018/9/13
N2 - An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.
AB - An analytical algebraic approach for distributed network identification is presented in this paper. The information propagation in the network is modeled using a state-space representation. Using the observations recorded at a single node and a known excitation signal, we present algorithms to compute the eigenfrequencies and eigenmodes of the graph in a distributed manner. The eigenfrequencies of the graph may be computed using a generalized eigenvalue algorithm, while the eigenmodes can be computed using an eigenvalue decomposition. The developed theory is demonstrated using numerical experiments.
UR - http://hdl.handle.net/10754/679445
UR - https://ieeexplore.ieee.org/document/8461484/
UR - http://www.scopus.com/inward/record.url?scp=85054214677&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2018.8461484
DO - 10.1109/ICASSP.2018.8461484
M3 - Conference contribution
SN - 9781538646588
SP - 4064
EP - 4068
BT - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PB - IEEE
ER -