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摘要: 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。Abstract: Vehicle yaw rates were measured by smartphone and high-precision inertial navigation system (INS). The influence of smartphone places on the measurement accuracy of yaw rate was analyzed. A self-adaptive weighted fusion algorithm was applied to reduce the measurement error of built-in gyroscope and orientation sensor of smartphone. The extreme value theory of multivariable function was used to obtain the optimal weighting factors of two sensors. The best value of yaw rate was calculated by weighted summation. Analysis result indicates that the impact of smartphone position on the measurement accuracy is very small. When smartphone is not fixed at center of gravity, the maximal relative errors of yaw rates measured by two sensors of smartphone are 0.739 7% and 0.923 8%, respectively. Average absolute error between fused data and INS data is 0.607 7 (°) ·s-1. Compared with the data measured by two sensors of smartphone, the average absolute error reduces by 34.3% and 50.0%, respectively. The variance of fused data declines and rapidly converges as the number of measurement increases. The convergent time is about 6 s.
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Key words:
- automotive engineering /
- vehicle yaw rate /
- smartphone /
- data fusion /
- self-adaptive weighted algorithm
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图 12 位置2方向传感器测量误差曲线
Figure 12. Measurement error curve of orientation sensor at location 2
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