Name
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3390/s17122810 | SENSORS |
Keywords | Field | DocType |
smart sensors,Kalman filters,algorithm complexity,IMU,compensation | Gyroscope,CPU time,Electronic engineering,Kalman filter,Sensor fusion,Inertial measurement unit,Microcontroller,Engineering,Algorithmic complexity,Computation | Journal |
Volume | Issue | ISSN |
17 | 12.0 | 1424-8220 |
Citations | PageRank | References |
4 | 0.40 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Valade, A. | 1 | 5 | 1.47 |
| P. Acco | 2 | 4 | 1.41 |
| Pierre Grabolosa | 3 | 4 | 0.40 |
| Jean-Yves Fourniols | 4 | 151 | 14.18 |