The paper addresses Kalman filtering over a peer-to-peer sensor network with a careful eye towards data transmission scheduling for reduced communication bandwidth and, consequently, enhanced energy efficiency and prolonged network lifetime. A novel consensus Kalman filter algorithm with event- triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when this is considered as particularly significant for estimation purposes, in the sense that it notably deviates from the information that can be predicted from the last transmitted one. Further, it is proved how the filter guarantees stability (mean-square boundedness of the estimation error in each node) under network connectivity and system collective observability. Finally, numerical simulations are provided to demonstrate practical effectiveness of the distributed filter for trading off estimation performance versus transmission rate.
A distributed Kalman filter with event-triggered communication and guaranteed stability / Battistelli, Giorgio*; Chisci, Luigi; Selvi, Daniela. - In: AUTOMATICA. - ISSN 0005-1098. - STAMPA. - 93:(2018), pp. 75-82. [10.1016/j.automatica.2018.03.005]
A distributed Kalman filter with event-triggered communication and guaranteed stability
Battistelli, Giorgio
Membro del Collaboration Group
;Chisci, LuigiMembro del Collaboration Group
;Selvi, DanielaMembro del Collaboration Group
2018
Abstract
The paper addresses Kalman filtering over a peer-to-peer sensor network with a careful eye towards data transmission scheduling for reduced communication bandwidth and, consequently, enhanced energy efficiency and prolonged network lifetime. A novel consensus Kalman filter algorithm with event- triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when this is considered as particularly significant for estimation purposes, in the sense that it notably deviates from the information that can be predicted from the last transmitted one. Further, it is proved how the filter guarantees stability (mean-square boundedness of the estimation error in each node) under network connectivity and system collective observability. Finally, numerical simulations are provided to demonstrate practical effectiveness of the distributed filter for trading off estimation performance versus transmission rate.File | Dimensione | Formato | |
---|---|---|---|
Automatica2018.pdf
Accesso chiuso
Descrizione: Main body
Tipologia:
Pdf editoriale (Version of record)
Licenza:
Tutti i diritti riservati
Dimensione
703.48 kB
Formato
Adobe PDF
|
703.48 kB | Adobe PDF | Richiedi una copia |
Automatica_data_driven.pdf
accesso aperto
Tipologia:
Versione finale referata (Postprint, Accepted manuscript)
Licenza:
Creative commons
Dimensione
384.68 kB
Formato
Adobe PDF
|
384.68 kB | Adobe PDF |
I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.