A Study about Kalman Filters Applied to Embedded Sensors
Aurélien Valade1,2, Pascal Acco3, Pierre Grabolosa4
1LAAS-CNRS, Université de Toulouse, CNRS, INSA, 31031 Toulouse, France. aurelien.valade@imerir.com.
Sensors (Basel, Switzerland)
|December 6, 2017
Summary
This study presents a low-power sensor data fusion methodology using multi-physical models and Kalman filters. It enhances precision and reliability for smart sensors, optimizing processing time on microcontrollers.
Area of Science:
- Embedded Systems
- Sensor Fusion
- Signal Processing
Background:
- Smart sensors increasingly integrate multiple measurement sources.
- Low-power processors present challenges for complex sensor data processing.
- Real-time constraints and computational efficiency are critical for embedded applications.
Purpose of the Study:
- To develop a methodology for accurate physical value estimation in low-power smart sensors.
- To optimize algorithmic complexity for real-time processing on resource-constrained microcontrollers.
- To improve sensor precision and reliability while minimizing power consumption.
Main Methods:
- Implementing a data fusion methodology using multi-physical models and Kalman filters.
- Focusing on algorithmic complexity to meet real-time and low-power constraints.
- Applying the methodology to estimate 2D orientation using an inertial measurement unit with gyroscope bias compensation.
Main Results:
- Demonstrated a methodology suitable for low-power STM32L053 microcontrollers.
- Achieved a processing time of 1.18 ms at 32 MHz with 3.8% CPU usage.
- Enabled a 26 Hz measurement and estimation rate, improving precision and reliability.
Conclusions:
- The proposed methodology effectively enhances sensor data processing on low-power devices.
- It balances computational load, precision, and reliability for smart sensor applications.
- Maximizes available processing time for other tasks on the microcontroller.
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