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• W2772201437 abstract "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." @default.
• W2772201437 created "2017-12-22" @default.
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• W2772201437 date "2017-12-05" @default.
• W2772201437 modified "2023-10-18" @default.
• W2772201437 title "A Study about Kalman Filters Applied to Embedded Sensors" @default.
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• W2772201437 doi "https://doi.org/10.3390/s17122810" @default.
• W2772201437 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/5751614" @default.
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