Dayo Oshinubi

Real-Time Autocalibration of MEMS Accelerometers(

In this paper, the self-calibration of micromechanical acceleration sensors is considered, specifically, based solely on user-generated movement data without the support of laboratory equipment or external sources. The autocalibration algorithm itself uses the fact that under static conditions, the squared norm of the measured sensor signal should match the magnitude of the gravity vector. The resulting nonlinear optimization problem is solved using robust statistical linearization instead of the common analytical linearization for computing bias and scale factors of the accelerometer. To control the forgetting rate of the calibration algorithm, artificial process noise models are developed and compared with conventional ones. The calibration methodology is tested using arbitrarily captured acceleration profiles of the human daily routine and shows that the developed algorithm can significantly reject any misconfiguration of the acceleration sensor.

Undersampling approach for a capacitive micromachined yaw rate sensor

Micromachined inertial sensors offer new applications and opportunities for improved mobile devices. Especially the consumer market for micromechanical motion sensing through gyroscopes is widely emerging. The integration of inertial sensors within portable devices enforces suitable power management concepts to handle and achieve the tight power budget of the electronic devices. This paper presents a digital system approach to reduce the power consumption for a capacitive micromachined yaw rate sensor and enables an energy efficient frontend operation of the sensor.