Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

作     者：陈伟 李旭 张为公 Chen Wei;Li Xu;Zhang Weigong

作者机构：东南大学仪器科学与工程学院南京210096 

出 版 物：《Journal of Southeast University(English Edition)》 (东南大学学报（英文版）)

年 卷 期：2013年第29卷第2期

页      面：170-174页

核心收录：

学科分类：082304[工学-载运工具运用工程] 08[工学] 080204[工学-车辆工程] 0802[工学-机械工程] 0823[工学-交通运输工程] 

基　　金：The National Natural Science Foundation of China(No.61273236) the Natural Science Foundation of Jiangsu Province(No.BK2010239) the Ph.D.Programs Foundation of Ministry of Education of China(No.200802861061) 

主　　题：microelectromechanical system (MEMS)gyroscope autoregressive integrated moving average(ARIMA) model time series analysis gross errors 

摘      要：This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.