In this paper, we present a new method to calibrate the misalignment error and zero offset of MEMS accelerometer, which applies the Genetic Algorithm (GA) to process measured data and get the error model parameters. This method can effectively eliminate the error caused by the assembly deviation between the sensitive sensor unit and the sensor package shell. Results show that the calibrated output is far more accurate than the raw data obtained by only used factory calibration. The mean squared error (MSE) before calibration is 1.754×10-3g2which reduces to 2.828×10-5g2. Furthermore, the proposed procedure shows more advantages than the BP Neural Network method. The genetic algorithm behaves convenient and suitable for the calibration problem of MEMS accelerometer and reduces effect of the misalignment error and zero offset.
展开▼
机译：在本文中，我们提出了一种新方法来校准MEMS加速度计的未对准误差和零偏移，其应用遗传算法（GA）来处理测量数据并获取错误模型参数。该方法可以有效地消除由敏感传感器单元和传感器包壳之间的装配偏差引起的误差。结果表明，校准的输出比仅通过使用的工厂校准所获得的原始数据更准确。校准前的平均平均误差（MSE）为1.754×10 -3 G 2 这减少到2.828×10 -5 G 2 。此外，所提出的程序表明了比BP神经网络方法更多的优点。遗传算法的行为行为适用于MEMS加速度计的校准问题，降低了未对准误差和零偏移的效果。
展开▼
