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Kalman Filtering Applied to Low-Cost Navigation Systems: A Preliminary Approach

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

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Abstract

The development of the technology in the last decades, and in particular of the navigation and positioning systems, with the appearance of the Micro-Electro-Mechanical Systems (MEMS) allowed solutions of positioning and navigation low-cost. The objective of this work is the construction of a low-cost positioning solution for small sailboats. Kalman filtering is used to process data from an MEMS inertial sensor and a GPS receiver on small sailing vessels. The validation of the work is done by comparing the results obtained by the low-cost system with those obtained by higher precision systems.

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Notes

  1. 1.

    The authors of [12] resume in a single paragraph the description of KF approach “...The Kalman Filter (KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which simply linearizes all nonlinear models so that the traditional linear Kalman filter can be applied. Although the EKF (in its many forms) is a widely used filtering strategy, over thirty years of experience with it has led to a general consensus within the tracking and control community that it is difficult to implement, difficult to tune, and only reliable for systems which are almost linear on the time scale of the update intervals....”.

  2. 2.

    MATLAB and Statistics Toolbox Release 2012b, The MathWorks, Inc., Natick, Massachusetts, United States.

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Acknowledgements

This work was supported by Portuguese funds through the Center for Computational and Stochastic Mathematics (CEMAT), The Portuguese Foundation for Science and Technology (FCT), University of Lisbon, Portugal, project UID/Multi/04621/2013, and Center of Naval Research (CINAV), Naval Academy, Portuguese Navy, Portugal.

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Authors and Affiliations

  1. CINAV, Portuguese Naval Academy, Portuguese Navy, Base Naval de Lisboa, Alfeite, 2810-001, Almada, Portugal

    José Vieira Duque, Victor Plácido da Conceição & M. Filomena Teodoro

  2. CEMAT - Center for Computational and Stochastic Mathematics, Instituto Superior Técnico, Lisbon University, Avenida Rovisco Pais, n. 1, 1048-001, Lisboa, Portugal

    M. Filomena Teodoro

Authors
  1. José Vieira Duque
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  2. Victor Plácido da Conceição
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  3. M. Filomena Teodoro
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Correspondence to M. Filomena Teodoro .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Duque, J.V., da Conceição, V.P., Teodoro, M.F. (2018). Kalman Filtering Applied to Low-Cost Navigation Systems: A Preliminary Approach. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_36

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  • DOI: https://doi.org/10.1007/978-3-319-95165-2_36

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