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使用智能手机和高精度惯导设备测量了车辆横摆角速度,分析了手机放置位置对测量精度的影响。针对智能手机的测量误差,采用自适应加权融合算法对智能手机中陀螺仪和方向传感器的测量数据进行融合修正。根据多元函数极值理论求出2个传感器的最优加权因子,加权求和得到最优的横摆角速度。分析结果表明:智能手机的放置位置对车辆横摆角速度测量精度影响很小,重心位置与非重心位置上的手机陀螺仪和方向传感器测量结果最大相对误差分别为0.739 7%和0.923 8%。融合修正后的数据与高精度惯导设备数据相比,平均绝对误差为0.607 7(°)·s-1,相比陀螺仪和方向传感器平均绝对误差分别降低了34.3%和50.0%。融合后的数据均方差随测量次数增加呈下降趋势,并快速收敛,收敛时间约为6s。
The use of smart phones and high-precision inertial navigation equipment measured vehicle yaw rate, the location of the phone placed on the measurement accuracy. Aiming at the measurement error of smart phones, the adaptive weighted fusion algorithm is used to fuse and reconstruct the measurement data of the gyroscope and the direction sensor in the smart phone. According to the extreme value theory of multiple functions, the optimal weighting factors of two sensors are obtained, and the optimal yaw rate is obtained by weighted summation. The analysis results show that the placement accuracy of the smartphone has little influence on the accuracy of the vehicle yaw rate, and the maximum relative errors of the measured results of the handset gyro and the direction sensor at the center of gravity and the non-center of gravity are 0.739 7% and 0.923 8% respectively. The average absolute error of fusion data is 0.607 7 (°) · s-1, which is 34.3% and 50.0% lower than that of gyroscope and directional sensor respectively. After the fusion, the mean square error of the data decreases with the increase of the number of measurements and converges rapidly, with a convergence time of about 6s.