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In Use Parameter Estimation of Inertial Sensors by Detecting Multilevel Quasi-static States

  • Ashutosh Saxena
  • Gaurav Gupta
  • Vadim Gerasimov
  • Sébastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3684)

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.

Keywords

Inertial Sensor Orientation Error Triaxial Accelerometer Dynamic Accuracy Angular Rate Sensor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ashutosh Saxena
    • 1
  • Gaurav Gupta
    • 2
  • Vadim Gerasimov
    • 3
  • Sébastien Ourselin
    • 2
  1. 1.Department of Electrical EngineeringStanford UniversityUSA
  2. 2.Autonomous Systems LaboratoryBioMedIA Lab, CSIRO ICT CentreEppingAustralia
  3. 3.Autonomous Systems LaboratoryCSIRO ICT CentreEppingAustralia

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