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https://hdl.handle.net/10953/3257
Title: | An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for T-Proper Systems with Multiple Packet Dropouts |
Authors: | Fernández Alcalá, Rosa María Jiménez López, José Domingo Le Bihan, Nicolas Cheong Took, Clive |
Abstract: | This paper analyses the centralized fusion linear estimation problem in multi-sensor systems with multiple packet dropouts and correlated noises. Packet dropouts are modeled by independent Bernoulli distributed random variables. This problem is addressed in the tessarine domain under conditions of 𝕋1 and 𝕋2-properness, which entails a reduction in the dimension of the problem and, consequently, computational savings. The methodology proposed enables us to provide an optimal (in the least-mean-squares sense) linear fusion filtering algorithm for estimating the tessarine state with a lower computational cost than the conventional one devised in the real field. Simulation results illustrate the performance and advantages of the solution proposed in different settings. |
Keywords: | centralized fusion estimation multi-sensor systems packet dropouts tessarine signal processing 𝕋k-properness |
Issue Date: | 17-Apr-2023 |
metadata.dc.description.sponsorship: | This paper has been supported in part by the Project PID2021-124486NB-I00 of the ‘Plan Estatal de I+D+i’, Ministerio de Educación y Ciencia, Spain, the I+D+i project with reference number 1256911, under ‘Programa Operativo FEDER Andalucía 2014–2020’, Junta de Andalucía, and Project EI-FQM2-2023 of ‘Plan de Apoyo a la Investigación 2023–2024’ from the University of Jaén. |
Publisher: | MDPI |
Citation: | Fernández-Alcalá RM, Jiménez-López JD, Le Bihan N, Cheong Took C. An Optimal Linear Fusion Estimation Algorithm of Reduced Dimension for 𝕋 -Proper Systems with Multiple Packet Dropouts. Sensors. 2023; 23(8):4047. https://doi.org/10.3390/s23084047 |
Appears in Collections: | DEIO-Artículos |
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sensors-23-04047 (1).pdf | Artículo publicado en Sensors en 2023 | 935,81 kB | Adobe PDF | View/Open |
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