A Study about Kalman Filters Applied to Embedded Sensors
AbstractOver 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
Externally hosted supplementary file 1
Description: Additional discussions about the measurements and source the developed source code.
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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.
Valade A, Acco P, Grabolosa P, Fourniols J-Y. A Study about Kalman Filters Applied to Embedded Sensors. Sensors. 2017; 17(12):2810.Chicago/Turabian Style
Valade, Aurélien; Acco, Pascal; Grabolosa, Pierre; Fourniols, Jean-Yves. 2017. "A Study about Kalman Filters Applied to Embedded Sensors." Sensors 17, no. 12: 2810.
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