Temperature compensation method of MEMS accelerometer with improved interpolation

To improve the temperature performance of accelerometer, this paper studied the temperature characteristics of MEMS accelerometer, investigated calibration methods of accelerometer and the error compensation model. We proposed a novel interpolation-based least square fitting compensation method, which firstly uses the interpolation method to predict temperature-zero bias and temperature-scale factor data, then compensating doubled data by the least square method. We carried out a compensation experiment and verified the effectiveness of each step in the proposed method. Testing results show that in the range of - 40°C to 60°C, through the use of the least square fitting method based on interpolation, the extreme difference of the scale factor and zero bias errors are reduced by 83.6%, 93.5% respectively. Meanwhile, the root means square error of the scale factor and zero bias errors are reduced 86.2% and 95.1% respectively. After temperature compensation, the fluctuation range of the upright placed accelerometer is reduced from 0.038g to 0.003g. It is proved that this method can reduce the measurement cost and obtain high precision compensation results, which is suitable for engineering applications.