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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Rosa M. Fernández-Alcalá & José D. Jiménez-López & Jesús Navarro-Moreno & Juan C. Ruiz-Molina, 2022. "Multisensor Fusion Estimation for Systems with Uncertain Measurements, Based on Reduced Dimension Hypercomplex Techniques," Mathematics, MDPI, vol. 10(14), pages 1-29, July.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2495-:d:865307
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