[1] Chen Wei, Li Xu, Zhang Weigong,. Gross errors identification and correctionof in-vehicle MEMS gyroscope based on time series analysis [J]. Journal of Southeast University (English Edition), 2013, 29 (2): 170-174. [doi:10.3969/j.issn.1003-7985.2013.02.011]
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Gross errors identification and correctionof in-vehicle MEMS gyroscope based on time series analysis(
)
基于时间序列分析的车载MEMS陀螺仪 异常测量数据的辨识与修正
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 29
- Issue:
- 2013 2
- Page:
- 170-174
- Research Field:
- Traffic and Transportation Engineering
- Publishing date:
- 2013-06-20
Info
- Title:
- Gross errors identification and correctionof in-vehicle MEMS gyroscope based on time series analysis
- 基于时间序列分析的车载MEMS陀螺仪 异常测量数据的辨识与修正
- Author(s):
- Chen Wei; Li Xu; Zhang Weigong
- School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China
- Keywords:
- microelectromechanical system(MEMS)gyroscope; autoregressive integrated moving average(ARIMA)model; time series analysis; gross errors
- PACS:
- U467
- DOI:
- 10.3969/j.issn.1003-7985.2013.02.011
- Abstract:
- 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(AIC), 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.
- 针对目前车载MEMS陀螺仪含有较多异常测量数据的情况, 提出了一种基于时间序列分析的辨识和修正方法.根据MEMS陀螺仪测量数据的自相关函数和偏相关函数特征初步确定自回归移动平均(ARIMA)模型, 再引入AIC准则确定最优模型, 并采用最小二乘估计法对模型参数进行估计.当此模型的有效性检验通过时, 即用该模型对测量数据的变化趋势进行预测.当某个测量值与其预测值之差大于设定的阈值时, 则判定此测量值为异常数据并用预测值进行修正.为了验证所提算法的效果, 对MEMS陀螺仪测量的横摆角速度数据进行了实验.结果表明, 所提方法可以有效地识别出车载MEMS陀螺仪的异常测量数据, 并能进行合理的修正.
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Memo
- Memo:
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Biographies: Chen Wei(1985—), male, graduate; Li Xu(corresponding author), male, doctor, associate professor, lixu.mail@163.com.
Foundation items: 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).
Citation: Chen Wei, Li Xu, Zhang Weigong. Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis[J].Journal of Southeast University(English Edition), 2013, 29(2):170-174.[doi:10.3969/j.issn.1003-7985.2013.02.011]