A K-Hop Graph-Based Observer for Large-Scale Networked Systems. Gasparri, A. & Marino, A. 2017. ![A K-Hop Graph-Based Observer for Large-Scale Networked Systems [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex In this paper, we address the state estimation problem for multi-agent systems interacting in large scale networks. This research is motivated by the observation that in large-scale networks for many practical applications and domains, each agent only requires information concerning agents spatially close to its location, let’s say topologically k-hop away. We propose a scalable framework where each agent is able to estimate in finitetime the state of its k-hop neighborhood by interacting only with the agents belonging to its 1-hop neighborhood.
@conference{
11580_71250,
author = {Gasparri, Andrea and Marino, Alessandro},
title = {A K-Hop Graph-Based Observer for Large-Scale Networked Systems},
year = {2017},
publisher = {IEEE},
booktitle = {56th IEEE Conference on Decision and Control},
abstract = {In this paper, we address the state estimation problem
for multi-agent systems interacting in large scale networks.
This research is motivated by the observation that in large-scale
networks for many practical applications and domains, each
agent only requires information concerning agents spatially close
to its location, let’s say topologically k-hop away. We propose a
scalable framework where each agent is able to estimate in finitetime
the state of its k-hop neighborhood by interacting only with
the agents belonging to its 1-hop neighborhood.},
keywords = {Distributed observer},
url = {https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8264361},
doi = {10.1109/CDC.2017.8264361},
isbn = {978-1-5090-2872-6},
pages = {4747--4752}
}
Downloads: 0
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