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.

Gasparri, A., & Marino, A. (2018). A k-hop graph-based observer for large-scale networked systems. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (pp.4747-4752). Institute of Electrical and Electronics Engineers Inc. [10.1109/CDC.2017.8264361].

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

Gasparri A.;
2018

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 finite-time the state of its k-hop neighborhood by interacting only with the agents belonging to its 1-hop neighborhood.
978-1-5090-2873-3
Gasparri, A., & Marino, A. (2018). A k-hop graph-based observer for large-scale networked systems. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (pp.4747-4752). Institute of Electrical and Electronics Engineers Inc. [10.1109/CDC.2017.8264361].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/353886
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