Angular Motion Estimation Using Dynamic Models in a Gyro-Free Inertial Measurement UnitReport as inadecuate

Angular Motion Estimation Using Dynamic Models in a Gyro-Free Inertial Measurement Unit - Download this document for free, or read online. Document in PDF available to download.


Center for Sensor Systems ZESS, University of Siegen, Paul Bonatz-Str. 9-11, 57068 Siegen, Germany


iMAR GmbH, St. Ingbert, Germany


Author to whom correspondence should be addressed.

Abstract In this paper, we summarize the results of using dynamic models borrowed from tracking theory in describing the time evolution of the state vector to have an estimate of the angular motion in a gyro-free inertial measurement unit GF-IMU. The GF-IMU is a special type inertial measurement unit IMU that uses only a set of accelerometers in inferring the angular motion. Using distributed accelerometers, we get an angular information vector AIV composed of angular acceleration and quadratic angular velocity terms. We use a Kalman filter approach to estimate the angular velocity vector since it is not expressed explicitly within the AIV. The bias parameters inherent in the accelerometers measurements’ produce a biased AIV and hence the AIV bias parameters are estimated within an augmented state vector. Using dynamic models, the appended bias parameters of the AIV become observable and hence we can have unbiased angular motion estimate. Moreover, a good model is required to extract the maximum amount of information from the observation. Observability analysis is done to determine the conditions for having an observable state space model. For higher grades of accelerometers and under relatively higher sampling frequency, the error of accelerometer measurements is dominated by the noise error. Consequently, simulations are conducted on two models, one has bias parameters appended in the state space model and the other is a reduced model without bias parameters. View Full-Text

Keywords: angular motion estimation; GF-IMU; dynamic models angular motion estimation; GF-IMU; dynamic models

Author: Ezzaldeen Edwan 1,* , Stefan Knedlik 2 and Otmar Loffeld 1



Related documents