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.
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.
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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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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