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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(ARI-MA)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.
Aiming at the current situation that the MEMS gyroscope contains many abnormal measurement data, a new method based on time series analysis is proposed.According to the autocorrelation function and the partial correlation function of MEMS gyroscope measurement data, the autoregressive moving average (ARI-MA) model, and then introduce the AIC criterion to determine the optimal model, and use the least square method to estimate the model parameters.When the validity of this model is tested, the model is used to measure the trend of the data Prediction.When the difference between a measured value and its predicted value is greater than the set threshold value, it is determined that the measured value is abnormal data and corrected with the predicted value.In order to verify the effectiveness of the proposed algorithm, the measured gyroscope yaw rate Data.The experimental results show that the proposed method can effectively identify the abnormal measurement data of MEMS gyroscope and can make reasonable correction.