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首页> 外文会议>Instrumentation and Measurement Technology Conference (I2MTC), 2012 IEEE International >Autocalibration of MEMS accelerometers
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Autocalibration of MEMS accelerometers

机译：MEMS加速度计的自动校准

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摘要

In this paper a new approach of an auto calibration method for micromechanical sensors is proposed. In particular, recalibration of acceleration sensors without any additional laboratory equipment is considered. If the device is stationary, the proposed procedure exploits the fact that the output vector of the acceleration sensor should match the gravity acceleration. The calibration method computes the scale factors and the bias components of the unbalanced acceleration sensor. These parameters are computed through nonlinear optimization. The applied optimization method is a nonlinear parameter estimator based on the Unscented Transformation. This methodology uses the robust statistical linearization instead of the common analytical linearization. In addition, the applied methodology minimizes the amount of temporarily stored measurement data which are mandatory to launch the recalibration algorithm. Reducing the amount of temporarily stored data is equivalent to reducing the memory space and the power required for the algorithm. An effective method for rejecting disturbance acceleration is also included in order to apply user generated data for the recalibration. First the calibration method is evaluated through simulations and second with real data generated by an acceleration sensor. The simulation results show that the algorithm estimates the offset and sensitivity parameters more precisely than the uncertainty introduced through the measurement noise.
机译：本文提出了一种新的微机械传感器自动校准方法。特别地，考虑了在没有任何附加实验室设备的情况下重新校准加速度传感器。如果设备是固定的，则建议的过程将利用以下事实：加速度传感器的输出矢量应与重力加速度匹配。校准方法计算不平衡加速度传感器的比例因子和偏置分量。这些参数是通过非线性优化计算的。应用的优化方法是基于Unscented变换的非线性参数估计器。该方法使用稳健的统计线性化，而不是常用的分析线性化。另外，所应用的方法使启动重新校准算法必需的临时存储的测量数据的数量最小化。减少临时存储的数据量等同于减少存储空间和算法所需的功耗。为了将用户生成的数据用于重新校准，还包括一种有效的方法来消除干扰加速。首先，通过仿真评估校准方法，其次使用加速度传感器生成的真实数据。仿真结果表明，与通过测量噪声引入的不确定性相比，该算法估计偏移和灵敏度参数的精度更高。

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