室内和室外气压测高的测试与分析

Shuaichen Li, Jian-Feng Wu
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摘要

测量场地高度对于科学研究和日常生活都具有重要意义。本文旨在测量室内外空气压力高度,对室内外实测数据的误差和波动进行比较分析,并用卡尔曼滤波对实测值进行滤波,减小误差和波动;分析了风速对测量误差和波动的影响;采用三种方法对测风组数据进行处理,并对处理结果进行比较;对气压传感器的可预测性进行了测试和分析。实验结果表明:1)与室内测量相比,室外测量的误差和波动明显更大。2)在室内外测量中,卡尔曼滤波效果良好,测量数据的平均误差最多减少31.9%,测量波动最多减少37.5%。实测数据对卡尔曼滤波的效果有很大影响。测量值的波动和误差越大,卡尔曼滤波的效果越明显。3)风对气压测高的影响较大,测量结果的波动和误差随风速的增大而增大。结合理论知识和对比实验结果,推测室外测量误差和波动较大的原因包括风对传感器的干扰。4)传统的最小二乘拟合效果最差,而基于卡尔曼滤波的最小二乘拟合效果最好。5)在有风状态下,二次拟合的均方根比无风状态高60%,残差高63.8%。
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Test and Analysis Of Indoor And Outdoor Barometric Altimetry
Measuring the height of a site is of great significance for scientific research and daily life. This paper aims to measure the indoor and outdoor air pressure height, compare and analyze the error and fluctuation of indoor and outdoor measured data, and filter the measured value with Kalman filter to reduce the error and fluctuation; The influence of wind speed on measurement error and fluctuation is analyzed; Three methods are used to process the data of the wind measurement group and the results are compared; The predictability of air pressure sensor is tested and analyzed. The experimental results show that: 1) Compared with indoor measurement, the error and fluctuation of outdoor measurement are significantly worse. 2) In indoor and outdoor measurement, Kalman filter has a good effect, which can reduce the average error of measurement data by 31.9% at most and the measurement fluctuation by 37.5%. The effect of Kalman filtering is greatly affected by the measured data. The greater the fluctuation and error of the measured value, the more obvious the effect of Kalman filter. 3) Wind has a great influence on barometric altimetry, and the fluctuation and error of the measurement results increase with the increase of wind speed. Combine theoretical knowledge with the results of comparative experiments It is speculated that the reason for the large outdoor measurement error and fluctuation includes the interference of wind to the sensor. 4) The traditional least squares fitting is the worst, and the least squares fitting based on Kalman filter is the best. 5) In the windy state, the RMS of quadratic fitting is 60% higher than that in the windless state, and the residual error is 63.8% higher.
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