基于快速小波变换的石英加速度计零偏预测

Prediction of quartz accelerometer zero-bias based on fast wavelet transform

针对石英挠性加速度计零偏在贮存期间受外界环境影响发生漂移的补偿问题,研究了基于快速小波变换的加速度计零偏预测方法。通过Mallat算法从非平稳的零偏序列中提取出平稳的细节序列和非线性趋势序列,再根据序列的特点分别采用自回归移动平均(ARMA)模型和径向基函数(RBF)神经网络进行预测建模;最后利用小波重构公式得到零偏预测值。为验证所提方法的有效性,对某型加速度计2年贮存条件下的零偏标定值进行了建模仿真。结果显示:组合模型较单一自回归综合移动平均(ARIMA)模型和RBF模型预测精度分别提升45.5%和47.4%。

Aiming at problem of drift compensation of quartz elastic accelerometer bias during storage caused by effect of external environment, a predictive method for accelerometer zero-bias, which based on fast wavelet transform, is studied. A stationary detail sequence and non-linear trend sequence are extracted from non-stationary zero-bias series by Mallat algorithm, according to characteristics of sequences, auto regressive moving average （ARMA） model and radial basis function（RBF） model are used for prediction and modeling; using wavelet reconstruction formula, predicted value of zero-bias is got. In order to verify the effectiveness of the proposed method,a 2-year natural storage environmental scale value of accelerometer zero-bias is modeling and simulated with the combined model. Experimental results show that the WT-ARMA-RBF model, compared with the single ARIMA model and RBF model , enhances the prediction precision about 45.5 % and 47.4 %.