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Sensors 2017, 17(12), 2810;

A Study about Kalman Filters Applied to Embedded Sensors

LAAS-CNRS, Université de Toulouse, CNRS, INSA, 31031 Toulouse, France
Institut Méditerranéen d’Enseignement et de Recherche en Informatique et Robotique, 66004 Perpignan, France
Author to whom correspondence should be addressed.
Received: 27 October 2017 / Revised: 20 November 2017 / Accepted: 28 November 2017 / Published: 5 December 2017
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Over the last decade, smart sensors have grown in complexity and can now handle multiple measurement sources. This work establishes a methodology to achieve better estimates of physical values by processing raw measurements within a sensor using multi-physical models and Kalman filters for data fusion. A driving constraint being production cost and power consumption, this methodology focuses on algorithmic complexity while meeting real-time constraints and improving both precision and reliability despite low power processors limitations. Consequently, processing time available for other tasks is maximized. The known problem of estimating a 2D orientation using an inertial measurement unit with automatic gyroscope bias compensation will be used to illustrate the proposed methodology applied to a low power STM32L053 microcontroller. This application shows promising results with a processing time of 1.18 ms at 32 MHz with a 3.8% CPU usage due to the computation at a 26 Hz measurement and estimation rate. View Full-Text
Keywords: smart sensors; Kalman filters; algorithm complexity; IMU; compensation smart sensors; Kalman filters; algorithm complexity; IMU; compensation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Valade, A.; Acco, P.; Grabolosa, P.; Fourniols, J.-Y. A Study about Kalman Filters Applied to Embedded Sensors. Sensors 2017, 17, 2810.

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