Stochastic Modeling of Inertial Sensors

This lecture covers the stochastic modeling of inertial sensors used in various applications like phones, watches, and unmanned aerial vehicles. The presentation introduces the Generalized Method of Wavelet Moments (GMWM) for optimal fusion with other devices. The software platform presented allows stochastic calibration of IMUs, providing statistically rigorous models for sensor signals. The lecture delves into the methodology behind GMWM, including processes like Quantization Noise, White Noise, Gauss Markov, Random Walk, and Drift. Practical examples and applications demonstrate the importance of precise sensor stochastics for improved navigation solutions. References to joint estimation, ambiguity fixing, and baseline determination are also discussed.