Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/30949
Title: Unscented Kalman Filtering Over Full-Duplex Relay Networks Under Binary Encoding Schemes
Authors: Wang, L
Wang, Z
Liu, S
Peng, D
Keywords: binary encoding schemes;bit flips;exponential mean-square boundedness;full-duplex relay networks;unscented Kalman filtering
Issue Date: 20-Dec-2024
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Citation: Wang, L. et al. (2024) 'Unscented Kalman Filtering Over Full-Duplex Relay Networks Under Binary Encoding Schemes', IEEE Transactions on Automatic Control, 0 (early access), pp. 1 - 8. doi: 10.1109/TAC.2024.3521281.
Abstract: In this paper, a modified unscented Kalman filter design algorithm is proposed for discrete-time stochastic nonlinear systems over full-duplex relay networks with binary encoding schemes. In order to enhance the transmission reliability, a full-duplex relay is deployed between sensors and the filter, and a self-interference cancellation scheme is introduced to eliminate the interference caused by the relay itself. To accommodate the digital communication manner, a binary encoding scheme is adopted, and a sequence of random variables obeying Bernoulli distribution is introduced to characterize statistical behaviors of the random bit flips. The objective of the addressed problem is to design an unscented Kalman filter over full-duplex relay networks with binary encoding schemes that reflects the impacts of the decoding error, the bit flips, and the full-duplex relay on the filtering performance. A sufficient condition is developed using the matrix inverse lemma to guarantee the exponential mean-square boundedness of the filtering error. Finally, a simulation study is carried out to demonstrate the effectiveness of the developed binary-encoding-based unscented Kalman filter over a full duplex network.
URI: https://bura.brunel.ac.uk/handle/2438/30949
DOI: https://doi.org/10.1109/TAC.2024.3521281
ISSN: 0018-9286
Other Identifiers: ORCiD: Licheng Wang https://orcid.org/0000-0001-5333-5881
ORCiD: Zidong Wang https://orcid.org/0000-0002-9576-7401
ORCiD: Shuai Liu https://orcid.org/0000-0003-0523-022X
ORCiD: Daogang Peng https://orcid.org/0009-0003-8677-644X
Appears in Collections:Dept of Computer Science Research Papers

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