In this paper we propose a solution to the problems of detecting a generally correlated stochastic output delay sequence of a linear system driven by Gaussian noise. This is the model for uncertain observations resulting from losses in the propagation channel due to fading phenomena or packet dropouts that is common in wireless sensor networks, networked control systems, or remote sensing applications. The solution we propose consists of a nonlinear detector which identifies online the stochastic delay sequence. The solution provided is optimal in the sense that minimizes the probability of error of the delay detector. Finally, a filtering stage fed with the information given by the detector can follow to estimate the state of the system. Numerical simulations show good performance of the proposed method.
Stochastic output delay identification and filtering of discrete-time gaussian systems / Battilotti, S.; D’Angelo, M.. - In: AUTOMATICA. - ISSN 0005-1098. - 109:(2019). [10.1016/j.automatica.2019.108499]
Stochastic output delay identification and filtering of discrete-time gaussian systems
S. Battilotti;M. d’Angelo
2019
Abstract
In this paper we propose a solution to the problems of detecting a generally correlated stochastic output delay sequence of a linear system driven by Gaussian noise. This is the model for uncertain observations resulting from losses in the propagation channel due to fading phenomena or packet dropouts that is common in wireless sensor networks, networked control systems, or remote sensing applications. The solution we propose consists of a nonlinear detector which identifies online the stochastic delay sequence. The solution provided is optimal in the sense that minimizes the probability of error of the delay detector. Finally, a filtering stage fed with the information given by the detector can follow to estimate the state of the system. Numerical simulations show good performance of the proposed method.File | Dimensione | Formato | |
---|---|---|---|
Battilotti_Preprint_Stochastic-output_2019.pdf
accesso aperto
Note: https://doi.org/10.1016/j.automatica.2019.108499
Tipologia:
Documento in Pre-print (manoscritto inviato all'editore, precedente alla peer review)
Licenza:
Creative commons
Dimensione
414.91 kB
Formato
Adobe PDF
|
414.91 kB | Adobe PDF | |
/Battilotti_Stochastic-output_2019.pdf
solo gestori archivio
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
Tutti i diritti riservati (All rights reserved)
Dimensione
493.15 kB
Formato
Adobe PDF
|
493.15 kB | Adobe PDF | Contatta l'autore |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.