Statistical Models of Inertial Sensors and Integral Error Bounds
Document Type
Article
Date of Original Version
1-1-2018
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.
Publication Title, e.g., Journal
STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics and Health
Citation/Publisher Attribution
Vaccaro, Richard J., and Ahmed S. Zaki. "Statistical Models of Inertial Sensors and Integral Error Bounds." STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics and Health (2018): 143-160. doi: 10.1007/978-3-319-95117-1_9.