Construction of Dynamically-Dependent Stochastic Error Models
The stochastic behavior of a navigation sensor is analyzed by constructing the Allan variance signatures from an error signal. For inertial sensors, such a signature is obtained by recording data at rest. The signal analysis results in suitable noise parameters. Nonetheless, those parameters may change under dynamic conditions. At first, Dr. Clausen presents the influence of rotational dynamics on MEMS gyroscopes. Next, he shows how to link this to the noise-parameter estimation in a rigorous way by a modified version of the Generalized Method of Wavelet Moments (GMWM). The results can then be used in a Kalman filter, where the noise parameters are adapted according to such a predetermined functional relationship between sensor noise and the encountered dynamics of the platform/sensor.
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- Date: 27 Aug 2019
- Time: 06:30 PM to 08:30 PM
- All times are (UTC+02:00) Bern
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- ETH
- Zurich, Switzerland
- Switzerland
- Building: HG
- Room Number: D 1.1
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- Co-sponsored by Swiss Institute of Navigation ION-CH
Speakers
Dr. Philipp Clausen
Construction of Dynamically-Dependent Stochastic Error Models
Biography:
Dr. Philipp Clausen obtained his PhD degree in Robotics at the Geodetic Engineering Laboratory (TOPO) under the supervision of Dr. Jan Skaloud at the Swiss Ecole Polytechnique Fédérale de Lausanne (EPFL). His research focused on navigation, sensor fusion, and calibration. He holds a Master Degree in Micro-Engineering from the EPFL. He is an IEEE member and volunteers as a Co-Chair of the Aerospace and Electronic Systems Chapter. He is currently employed at u-blox, Thawil.
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