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. 2017 Dec 5;17(12):2810.
doi: 10.3390/s17122810.

A Study about Kalman Filters Applied to Embedded Sensors

Aurélien Valade  1   2 , Pascal Acco  3 , Pierre Grabolosa  4 , Jean-Yves Fourniols  5
Affiliations

A Study about Kalman Filters Applied to Embedded Sensors

Aurélien Valade et al. Sensors (Basel). .

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.

Keywords: IMU; Kalman filters; algorithm complexity; compensation; smart sensors.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The Kalman filter recursive process.
Figure 2
Figure 2
Linear projection methods for: (a) linear system (Kalman projection); and (b) non-linear system local linearization (EKF projection).
Figure 3
Figure 3
Non-Linear projection methods for: (a) local linearization system (EKF projection); and (b) non-linear weighted projections (Unscented Transform projection).
Figure 4
Figure 4
Self-balancing robot orientation frame.
Figure 5
Figure 5
Gravity vector projection into the XZ plane.
Figure 6
Figure 6
2D orientation estimation Kalman filter processing time.
See this image and copyright information in PMC

References

    1. Kahn J.M., Katz R.H., Pister K.S.J. Next Century Challenges: Mobile Networking for “Smart Dust”; Proceedings of the 5th Annual ACM/IEEE International Conference on Mobile Computing and Networking; Seattle, WA, USA. 15–19 August 1999; pp. 271–278.
    1. Gil D., Ferrández A., Mora-Mora H., Peral J. Internet of Things: A review of Surveys Based on Context Aware Intelligent Services. Sensors. 2016;16:1069. doi: 10.3390/s16071069. - DOI - PMC - PubMed
    1. Khattak A.M., Truc P.T.H., Hung L.X., Vinh L.T., Dang V.-H., Guan D., Pervez Z., Han M., Lee S., Lee Y.-K. Towards Smart Homes Using Low Level Sensory Data. Sensors. 2011;11:11581–11604. doi: 10.3390/s111211581. - DOI - PMC - PubMed
    1. Zhu H., Gao L., Li H. Secure and Privacy-Preserving Body Sensor Data Collection and Query Scheme. Sensors. 2015;16:179. doi: 10.3390/s16020179. - DOI - PMC - PubMed
    1. Facchinetti A. Continuous Glucose Monitoring Sensors: Past, Present and Future Algorithmic Challenges. Sensors. 2016;16:2093. doi: 10.3390/s16122093. - DOI - PMC - PubMed