In use parameter estimation of inertial sensors by detecting multilevel quasi-static states

Authors:
Ashutosh Saxena;Gaurav Gupta;Vadim Gerasimov;Sébastien Ourselin
Affiliations:
Department of Electrical Engineering, Stanford University;Autonomous Systems Laboratory, BioMedIA Lab, CSIRO ICT Centre, Epping, NSW, Australia;Autonomous Systems Laboratory, CSIRO ICT Centre, Epping, NSW, Australia;Autonomous Systems Laboratory, BioMedIA Lab, CSIRO ICT Centre, Epping, NSW, Australia
Venue:
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Year:
2005

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Abstract

We present an autoadaptive algorithm for in-use parameter estimation of MEMS inertial accelerometers and gyros using multi-level quasi-static states for greater accuracy and reliability. Multi-level quasi-static states are detected robustly using data from both gyros and accelerometers. Proper estimation of time-varying sensor parameters allows us to develop a mixed-reality real-time hand-held orientation tracker with dynamic accuracy of less than 20. Existing methods like Kalman filters do not take time-varying nature of parameters into account, instead modelling the time-variation as higher values in noise covariance matrices; thus underestimating the sensor capabilities.