Fernández-Alcalá, Rosa MaríaJiménez-López, José DomingoNavarro-Moreno, JesúsRuiz-Molina, Juan Carlos2025-07-312025-07-312022-07-18https://doi.org/10.3390/math10142495https://hdl.handle.net/10953/6020The 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.engAttribution-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nd/3.0/es/Hypercomplex algebraMissing measurementsMulti-sensor information fusion estimationRandom delayed measurements𝕋k-proper signalsinfo:eu-repo/semantics/article519.8:621.382info:eu-repo/semantics/openAccess