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EnglishAn event-triggered consensus filter is proposed in this letter for state estimation in distributed sensor networks based on the hybrid consensus on measurement and consensus on information scheme. For bandwidth reduction and energy saving, an event-triggered transmission strategy is developed in which each node selectively transmits only the most relevant data so as to reduce data transmission while preserving the filtering performance. Two different transmission tests are performed in parallel, respectively on the prior and on the likelihood information pair, to evaluate the information loss (measured in terms of Kullback-Leibler divergence) that would be incurred if the current values were replaced by the predicted ones according to the last transmitted data. Simulation results on a distributed target tracking case-study demonstrate outperformance of the proposed filter with respect to conventional triggered filters
An Event-Triggered Hybrid Consensus Filter for Distributed Sensor Network / Xiangxiang Dong, Giorgio Battistelli, Luigi Chisci, Yunze Cai. - In: IEEE SIGNAL PROCESSING LETTERS. - ISSN 1070-9908. - STAMPA. - --:(In corso di stampa), pp. 1-5. [10.1109/LSP.2022.3183494]
| Titolo: | An Event-Triggered Hybrid Consensus Filter for Distributed Sensor Network | |
| Autori di Ateneo: | ||
| Autori: | Xiangxiang Dong; Giorgio Battistelli; Luigi Chisci; Yunze Cai | |
| Anno di registrazione: | Being printed | |
| Rivista: | ||
| Volume: | -- | |
| Pagina iniziale: | 1 | |
| Pagina finale: | 5 | |
| Abstract: | An event-triggered consensus filter is proposed in this letter for state estimation in distributed sensor networks based on the hybrid consensus on measurement and consensus on information scheme. For bandwidth reduction and energy saving, an event-triggered transmission strategy is developed in which each node selectively transmits only the most relevant data so as to reduce data transmission while preserving the filtering performance. Two different transmission tests are performed in parallel, respectively on the prior and on the likelihood information pair, to evaluate the information loss (measured in terms of Kullback-Leibler divergence) that would be incurred if the current values were replaced by the predicted ones according to the last transmitted data. Simulation results on a distributed target tracking case-study demonstrate outperformance of the proposed filter with respect to conventional triggered filters | |
| Handle: | http://hdl.handle.net/2158/1274629 | |
| Appare nelle tipologie: | 1a - Articolo su rivista |
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