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A Study about Kalman Filters Applied to Embedded Sensors.

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.

SUBMITTER: Valade A

PROVIDER: S-EPMC5751614 | biostudies-other | 2017 Dec

REPOSITORIES: biostudies-other

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A Study about Kalman Filters Applied to Embedded Sensors.

Valade Aurélien A Acco Pascal P Grabolosa Pierre P Fourniols Jean-Yves JY

Sensors (Basel, Switzerland) 20171205 12

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 reliabilit ...[more]

PMID: 29206187

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