Automatic modeling algorithm of stochastic error for inertial sensors
Luodi Zhao
Long Zhao
School of Automation Science and Electrical Engineering,Beihang University,Beijing 100191,China;Digital Navigation Center,Beihang University,Beijing 100191,China;Science and Technology on Aircraft Control Laboratory,Beihang University,Beijing 100191,China
摘要：This paper proposes an automatic algorithm to determine the properties of stochastic processes and their parameters for inertial error.The proposed approach is based on a recently developed method called the generalized method of wavelet moments(GMWM),whose estimator was proven to be consistent and asymptotically normally distributed.This algorithm is suitable mainly(but not only)for the combination of several stochastic processes,where the model identification and parameter estimation are quite difficult for the traditional methods,such as the Allan variance and the power spectral density analysis.This algorithm further explores the complete stochastic error models and the candidate model ranking criterion to realize automatic model identification and determination.The best model is selected by making the trade-off between the model accuracy and the model complexity.The validation of this approach is verified by practical examples of model selection for MEMS-IMUs(micro-electro-mechanical system inertial measurement units)in varying dynamic conditions.
机标关键词：algorithmmodelingautomaticerrorinertialsensorsstochastic
论文发表日期：2024-02-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：11( 81-91 )
英文信息