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Tuning Process Noise in INS/GNSS Fusion for Drone Navigation Based on Evolutionary Algorithms

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18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023) (SOCO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 750))

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Abstract

Tuning the navigation systems is a complex engineering task due to the sensitivity of their parameters, specific requirements to be met, as well as the maneuvering context that the system is designed to address. This problem is still a challenge for navigation system engineers who spend a lot of time to tests and simulations to achieve tuning. Tuning methodologies aim to find the best filter configurations considering a set of design requirements. It is important to note that the navigation system is a critical system within flight controllers since it is responsible to provide service to other subsystems such as control or guidance. The aim of this work is to fine-tune an INS/GNSS navigation system to ensure maximum position and/or orientation accuracy for specific missions with several maneuvers. We propose to use single and multiple objective evolutionary heuristic optimization strategies to carry out this problem. Finals results improve the navigation system and shows outstanding results compared to commercial tool method.

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Funding

This research was partially funded by public research projects of Spanish Ministry of Science and Innovation, references PID2020-118249RB-C22 and PDC2021-121567-C22 - AEI/10.13039/501100011033, and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M in the line of Excellence of University Professors, reference EPUC3M17.

Author information

Authors and Affiliations

  1. Applied Artificial Intelligence Group, University Carlos III of Madrid, Madrid, Spain

    Juan Pedro Llerena, Jesús García & José Manuel Molina

  2. Institute of Communications and Navigation, German Aerospace Center (DLR), Neustrelitz, Germany

    Daniel Arias

Authors
  1. Juan Pedro Llerena
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  2. Jesús García
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  3. José Manuel Molina
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  4. Daniel Arias
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Corresponding author

Correspondence to Juan Pedro Llerena .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  2. School of Industrial, Computer, University of Leon, León, Spain

    Hilde Pérez García

  3. Department of Mechanical Engineering, University of La Rioja, Logroño, Spain

    Francisco Javier Martínez de Pisón

  4. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Francisco Martínez Álvarez

  5. Data Science and Big Data Lab, Pablo de Olavide University, Seville, Spain

    Alicia Troncoso Lora

  6. Applied Computational Intelligence, University of Burgos, Burgos, Spain

    Álvaro Herrero

  7. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    José Luis Calvo Rolle

  8. Department of Industrial Engineering, University of A Coruña, A Coruña, Spain

    Héctor Quintián

  9. Faculty of Science, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Llerena, J.P., García, J., Molina, J.M., Arias, D. (2023). Tuning Process Noise in INS/GNSS Fusion for Drone Navigation Based on Evolutionary Algorithms. In: García Bringas, P., et al. 18th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2023). SOCO 2023. Lecture Notes in Networks and Systems, vol 750. Springer, Cham. https://doi.org/10.1007/978-3-031-42536-3_5

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