Multisensor Fusion Estimation for Systems with Uncertain Measurements, Based on Reduced Dimension Hypercomplex Techniques

Autor: Rosa M. Fernández-Alcalá, José D. Jiménez-López, Jesús Navarro-Moreno, Juan C. Ruiz-Molina
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Mathematics, Vol 10, Iss 14, p 2495 (2022)
Druh dokumentu: article
ISSN: 10142495
2227-7390
DOI: 10.3390/math10142495
Popis: The prediction and smoothing fusion problems in multisensor systems with mixed uncertainties and correlated noises are addressed in the tessarine domain, under Tk-properness conditions. Bernoulli distributed random tessarine processes are introduced to describe one-step randomly delayed and missing measurements. Centralized and distributed fusion methods are applied in a Tk-proper setting, k=1,2, which considerably reduce the dimension of the processes involved. As a consequence, efficient centralized and distributed fusion prediction and smoothing algorithms are devised with a lower computational cost than that derived from a real formalism. The performance of these algorithms is analyzed by using numerical simulations where different uncertainty situations are considered: updated/delayed and missing measurements.
Databáze: Directory of Open Access Journals
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