卡尔曼滤波算法在MEMS热式风速风向传感器中的应用  

Application of Kalman Filter Algorithm in MEMS Thermal Wind Speed and Direction Sensor

作　　者：赵天 田润 易真翔 秦明[1] ZHAO Tian;TIAN Run;YI Zhenxiang;QIN Ming(School of Electronic Science and Engineering,Southeast University,Nanjing Jiangsu 210096,China)

机构地区：[1]东南大学电子科学与工程学院,江苏南京210097

出　　处：《传感技术学报》2021年第2期183-188,共6页Chinese Journal of Sensors and Actuators

基　　金：国家重点研发计划项目(2018YFF01010600)。

摘　　要：针对MEMS热式风速风向传感器在实际使用过程中因噪声问题导致输出不稳定的现象,本文首次采用了卡尔曼(Kalman)滤波算法对本征白噪声进行滤波处理,从而优化传感器的输出性能。本文分别通过理论分析和仿真模拟证明了Kalman滤波算法的可行性。实验结果表明,通过调节相关参数,Kalman滤波算法在滤波效果、响应速度及误差上均优于传统的加权滑动均值滤波算法。除此之外,在消耗较少资源的前提下,Kalman滤波算法可以有效减少本征白噪声的干扰,且调节空间较大,可以适应不同的应用需求。To solve the problem that the output of the thermal wind speed and direction sensor is unstable due to the noise in practice,the Kalman filter algorithm is first applied to filter the intrinsic white noise and optimize the output performance of the sensor.In this paper,theoretical analysis and simulation both verify the feasibility of Kalman filtering algorithm.The experimental results demonstrate that,by adjusting the parameters,the Kalman filtering algorithm can achieve better effect,faster response speed and less error than the weighted moving average filtering algorithm which is commonly used.Moreover,the Kalman filtering algorithm can effectively reduce the influence of intrinsic white noise while consuming less resources,and the algorithm has a large adjustment space and can adapt to different needs.

关 键 词：微机械系统(MEMS) 热式风速风向传感器 单片机控制 卡尔曼滤波算法 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]