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Statistical Models of Inertial Sensors and Integral Error Bounds

  • Richard J. Vaccaro
  • Ahmed S. ZakiEmail author
Chapter
Part of the STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health book series (STEAM)

Abstract

Inertial sensors such as gyroscopes and accelerometers are important components of inertial measurement units (IMUs). Sensor output signals are corrupted by additive noise plus a random drift component. This drift component, also called bias, is modeled using different types of random processes. This chapter considers the random components that are useful for modeling modern tactical-graded MEMS sensors. These components contribute to errors in the first and second integrals of the sensor output. The main contribution of this chapter is the derivation of a statistical bound on the magnitude of the error in the integral of a sensor signal due to noise and drift. This bound is a simple function of the Allan variance of a sensor.

Keywords

Drift Component Allan Variance Plot Calibration Signal Weighted Least Squares Estimator Altruistic Variant 
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

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2018

Authors and Affiliations

  1. 1.Department of Electrical, Computer, and Biomedical EngineeringUniversity of Rhode IslandKingstonUSA
  2. 2.Naval Undersea Warfare CenterDivision NewportNewportUSA

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