MEMS传感器数据漂移抑制技术研究

Research on random drift suppression technology of MEMS sensor

针对惯性测量系统中MEMS加速度传感器存在信号漂移而导致测量误差的问题,采用时间序列的分析方法,对MEMS加速度传感器测量的数据进行分析。将MEMS加速度传感器测量的数据通过DSP读取后,通过ADF准则进行平稳性检验,传感器数据满足平稳时间序列条件。根据传感器数据的自相关函数与偏自相关函数特征,判断出序列满足AR(p)模型。通过AIC准则进行随机性检验,同时进行时间序列模型识别与参数估计,传感器数据在使用AR(1)模型进行建模时达到最优。建立MEMS加速度传感器信号漂移AR(1)模型,并依据模型设计卡尔曼滤波器。结果表明,在滤波前加速度传感器零偏稳定性为0.3032 mg,卡尔曼滤波后的加速度传感器零偏稳定性为0.0247 mg,测量稳定性能有效提高,并且运算阶数较低,能很好的应用于嵌入式系统。

Aiming at the problem of measurement error caused by signal drift of MEMS acceleration sensor in inertial measurement system,the measured data of MEMS acceleration sensor are analyzed by time series analysis method.After reading the data measured by MEMS acceleration sensor through DSP,the stability is tested by ADF criterion.The sensor data meets the stationary time series conditions.According to the characteristics of autocorrelation function and partial autocorrelation function of sensor data,it is judged that the sequence satisfies AR(P)model.Through AIC criterion for randomness test,time series model identification and parameter estimation,the sensor data is optimized by using AR(1)model.The signal drift AR(1)model of MEMS acceleration sensor is established,and the Kalman filter is designed according to the model.The results show that the zero bias stability of the acceleration sensor before filtering is 0.3032 mg,and the zero bias stability of the acceleration sensor after Kalman filtering is 0.0247 mg.The measurement stability is effectively improved,and the operation order is low,which can be well applied to the embedded system.