A k-hop graph-based observer for large-scale networked systems

A k-hop graph-based observer for large-scale networked systems. Gasparri, A. & Marino, A. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, volume 2018-January, 2017.
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© 2017 IEEE.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 finite-time the state of its k-hop neighborhood by interacting only with the agents belonging to its 1-hop neighborhood.

@inproceedings{Gasparri2017,
  author = {Gasparri, Andrea and Marino, Alessandro},
  title = {A k-hop graph-based observer for large-scale networked systems},
  booktitle = {2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017},
  volume = {2018-January},
  doi = {10.1109/CDC.2017.8264361},
  abstract = {© 2017 IEEE.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 finite-time the state of its k-hop neighborhood by interacting only with the agents belonging to its 1-hop neighborhood.},
  year = {2017},
}

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