Please use this identifier to cite or link to this item: 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
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