摘要
微机电系统(MEMS)惯性传感器在生产过程中需要进行高低温测试和全温度范围的数据补偿,使其性能保持稳定。以往的传感器补偿设备抗干扰能力弱,补偿模型适应性差,无法满足传感器批量生产的需要。通过研究MEMS惯性传感器补偿流程,设计了一种针对某型MEMS惯性传感器的高低温补偿系统,实现传感器的自动测试和补偿。通过引入相关算法实现了基于误差辨识的参数优化最小二乘支持向量机(LS-SVM)算法。算法实现了对误差和干扰的自动识别。实验结果表明:所提方法能够提高MEMS惯性传感器批量化高低温补偿的精度。
Micro-electro-mechanical system(MEMS)inertial sensors need high and low temperature test and data compensation in full temperature range during production to keep their performance stable.The previous sensor compensation equipment has weak anti-interference ability and poor adaptability of compensation model,which can not meet the needs of mass production of sensors.Therefore,by studying the compensation process of MEMS inertial sensor,a kind of high and low temperature compensation system for a certain type of MEMS inertial sensor is designed to realize automatic test and compensation of the sensor.The system implements the least square support vector machine(LS-SVM)algorithm for parameters optimization based on error identification by introducing correlation algorithm.The algorithm realizes automatic recognition of error and interference.Experimental results show that this method can improve the precision of batch high and low temperature compensation for MEMS inertial sensors.
作者
张和铭
秦刚
梁渊
马卓
张晓天
陈笑颖
ZHANG Heming;QIN Gang;LIANG Yuan;MA Zhuo;ZHANG Xiaotian;CHEN Xiaoying(School of Photo-Electronic Engineering,Xi’an Technological University,Xi’an 710021,China)
出处
《传感器与微系统》
CSCD
北大核心
2023年第9期153-156,共4页
Transducer and Microsystem Technologies
关键词
微机电系统惯性传感器
最小二乘支持向量机
参数优化
误差辨识
MEMS inertial sensor
least square support vector machine(LS-SVM)
parameter optimization
error identification
