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

Rosa M. Fernández-Alcalá, José D. Jiménez-López, Jesús Navarro-Moreno and Juan C. Ruiz-Molina
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Rosa M. Fernández-Alcalá: Department of Statistics and Operations Research, University of Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain
José D. Jiménez-López: Department of Statistics and Operations Research, University of Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain
Jesús Navarro-Moreno: Department of Statistics and Operations Research, University of Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain
Juan C. Ruiz-Molina: Department of Statistics and Operations Research, University of Jaén, Paraje Las Lagunillas, 23071 Jaén, Spain

Mathematics, 2022, vol. 10, issue 14, 1-29

Abstract: The prediction and smoothing fusion problems in multisensor systems with mixed uncertainties and correlated noises are addressed in the tessarine domain, under T k -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 T k -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.

Keywords: hypercomplex algebra; missing measurements; multi-sensor information fusion estimation; random delayed measurements; ? k -proper signals (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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