A Study about Kalman Filters Applied to Embedded Sensors - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Sensors Année : 2017

A Study about Kalman Filters Applied to Embedded Sensors

Résumé

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
Fichier principal
Vignette du fichier
sensors-17-02810-v2.pdf (936.4 Ko) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte
Loading...

Dates et versions

hal-01694026 , version 1 (25-06-2019)

Identifiants

Citer

Aurelien Valade, Pascal Acco, Pierre Grabolosa, Jean-Yves Fourniols. A Study about Kalman Filters Applied to Embedded Sensors. Sensors, 2017, 17 (12), pp.2810. ⟨10.3390/s17122810⟩. ⟨hal-01694026⟩
143 Consultations
1625 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More