三维流场测量中的微风量多传感器数据融合

doi:10.16180/j.cnki.issn1007-7820.2021.12.014

Abstract

Abstract:

Flow field in the enclosed three-dimensional space is very important for the design of the ventilation system. In view of the data processing of the air velocity sensor array composed of multiple sensors, a multi-sensor data fusion algorithm based on the correlation function-Kalman filter algorithm is proposed in this study. Invalid data acquired by flow sensors is excluded by correlation judgement in measuring. Then, the sensor calibration output data and variance are used as the initial estimated value and variance estimation of Kalman filter to perform the multi-sensor data fusion. Compared with the measurement of common sensor calibration, the measurement error of air velocity obtained by this method is smaller. The experimental results show that the method can effectively improve the measurement accuracy, and the experimental results of the three-dimensional flow field measurement based on the data processing method are accurate and reliable.

Key words: air velocity, ventilation, flow field, measurement, sensor, calibration, filter, data fusion

CLC Number:

TP393

XIAO Xinzhao,LIU Jianxu,WU Guojing,FU Dongxiang. Multi-Sensor Data Fusion in Measurement of Flow Field of Air Velocity in Three-Dimensional Large Space[J].Electronic Science and Technology, 2021, 34(12): 81-86.

Figures/Tables 6

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References 18

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Multi-Sensor Data Fusion in Measurement of Flow Field of Air Velocity in Three-Dimensional Large Space

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