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.
|Original language||English (US)|
|Title of host publication||2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)|
|Number of pages||5|
|State||Published - Sep 13 2018|
Bibliographical noteKAUST 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.