| 引用本文: | 周凯月,李佳,王玮冰,陈大鹏.多位置检测实现MEMS加速度计的自校准[J].哈尔滨工业大学学报,2021,53(12):114.DOI:10.11918/202005116 |
| ZHOU Kaiyue,LI Jia,WANG Weibing,CHEN Dapeng.Self-calibration of MEMS accelerometer based on multi-position detection method[J].Journal of Harbin Institute of Technology,2021,53(12):114.DOI:10.11918/202005116 |
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| 多位置检测实现MEMS加速度计的自校准 |
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周凯月1,2,李佳1,2,王玮冰1,2,陈大鹏1,2
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(1.中国科学院大学,北京 100049; 2.中国科学院 微电子研究所,北京 100029)
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| 摘要: |
| 为实现微机电系统（micro-electro-mechanical system,MEMS）加速度计在应用过程中的实时补偿校准,以保证应用需求的高精度输出,本研究在建立测量值与真实值之间的加速度自校准模型的基础上,对加速度计在任意位置下的多组静态观测数据样本进行筛选,结合LM（levenberg-marquardt）算法和最小二乘法模型参数,优化了LM算法中过度依赖初值的问题。对于任意位置下的加速度计静态输出数据,滤波后筛选出可用于最小二乘法的姿态数据,用来修正部分或者全部第k次迭代模型参数,作为第k+1次迭代的初值；其他姿态数据用于LM算法训练拟合第k+1次迭代模型参数,实现加速度计应用过程中的闭环、实时校准。以智能鞋垫应用为例,本研究对比了传统十二面体法、椭球法、单纯LM算法和LM&最小二乘法自校准法对加速度计的校准结果。结果表明,在智能鞋垫的长期使用中,本研究提出的LM&最小二乘法自校准法消除了由于LM初值设定引起的模型参数解算不精准的情况,并可实现实时采集、实时解算、实时校准的目标,能够达到与传统标定方法相同量级的姿态精度。 |
| 关键词: 微机电系统 加速度计 自校准 LM算法 十二面体法 椭球拟合法 |
| DOI：10.11918/202005116 |
| 分类号:TN98;TN965 |
| 文献标识码:A |
| 基金项目:国家重点研发计划(2018YFB2002700),中国科学院战略性先导科技专项(A类)项目(XDA22020100)；中科院资助项目(201510280052 XMXX201200019933) |
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| Self-calibration of MEMS accelerometer based on multi-position detection method |
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ZHOU Kaiyue1,2,LI Jia1,2,WANG Weibing1,2,CHEN Dapeng1,2
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(1.University of Chinese Academy of Sciences, Beijing 100049, China; 2.Institute of Microelectronics, Chinese Academy of Sciences, Beijing 100029, China)
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| Abstract: |
| To ensure the high precision output of micro-electro-mechanical system (MEMS) accelerometers, the real-time compensation calibration needs to be implemented in the application process. In this study, an acceleration self-calibration model between the measured value and the real value was established, and multiple sets of static observation samples of the accelerometer at different positions were filtered. The Levenberg-Marquardt (LM) algorithm was combined with the least squares method to calculate the model parameters, which solved the problem of initial value dependence of the LM algorithm. For the static output of the accelerometer at different positions, the posture data that can be used for the least squares method were selected after filtering, which were applied to modify part or all the kth iteration model parameters, being the initial value of the (k+1)th iteration; other posture data were used to train the (k+1)th model parameters by the LM algorithm, realizing the closed-loop and real-time calibration of the accelerometer in application. Taking the application of intelligent insoles as an example, the calibration results of traditional 12-position method,ellipsoid fitting method, LM algorithm, and LM & least squares method were compared. Experimental results show that in the long-term use of intelligent insoles, the proposed LM & least squares method could eliminate the inaccurate calculation of model parameters due to the setting of the initial value of LM algorithm. Besides, it could realize the real-time acquisition, calculation, and calibration of the target, and achieve the precision of the same magnitude as the traditional calibration methods. |
| Key words: MEMS accelerometer self-calibration LM algorithm 12-position method ellipsoid fitting method |
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