Heading enhancement algorithm of GNSS/IMU integrated navigation based on dual-antenna TDCP
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摘要:
在全球导航卫星系统(GNSS)和惯性测量单元件(IMU)组合导航系统中，姿态估计尤其是航向角的准确估计对于车辆行驶状态的实时监控尤为重要。但由于IMU在高度通道上是发散的，若不加以准确约束其误差会逐渐累积，因此，在航向角变化频繁的车载应用中其航向角的估计精度不足。为解决传统GNSS与IMU松组合方式导航姿态估计精度较差的问题，提出一种基于双天线历元间载波相位差分(TDCP)的GNSS/IMU组合导航航向角增强算法。该算法通过双天线TDCP求解车辆航向角来增加组合导航滤波融合观测值输入维度，并利用Hatch滤波和抗差自适应滤波分别实现观测域伪距精度提升和GNSS/IMU组合导航定位、定姿性能的提升。实验结果表明：所提算法相对于传统GNSS/IMU组合导航方法在三维方向上的定位、测速精度分别提高了22.12%、41.27%，车辆航向角精度提高了46.29%。
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关键词:
- GNSS/IMU组合导航 /
- 多天线 /
- 双天线历元间载波相位差分 /
- 抗差卡尔曼滤波 /
- 姿态精度
Abstract:In global navigation satellite system (GNSS) and inertial measurement units (IMU) integrated navigation systems, attitude estimation, especially the accurate heading estimation, is very important for the real-time monitoring of vehicle state. However, due to the divergence of IMU on the altitude channel, its errors will gradually accumulate if the IMU cannot be accurately constrained, so the estimation accuracy of the heading is insufficient in the vehicle application where the heading changes frequently. In order to solve the problem of poor attitude estimation accuracy in GNSS and IMU loosely coupled navigation, a heading enhancement algorithm of GNSS/IMU integrated navigation based on dual-antenna time-differenced carrier phase (TDCP) is proposed. The vehicle heading is solved by dual-antenna TDCP to increase the input dimension of observations in integrated navigation filtering, and the Hatch filter and robust adaptive filter are used to improve the pseudorange accuracy of the observation domain and the GNSS/IMU integrated navigation positioning and attitude estimation performance, respectively. The algorithm evaluation results based on the measured data show that compared with the traditional GNSS/IMU integrated navigation method, the proposed algorithm improves the positioning and velocity measurement accuracy by 22.12% and 41.27%, respectively, and the heading accuracy by 46.29%.
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Key words:
- GNSS/IMU integrated navigation /
- multiple antennas /
- TDCP /
- robust Kalman filtering /
- attitude accuracy
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图 5 实验车及搭载的实验设备
Figure 5. The experimental vehicle and the experimental equipment onboard
图 10 不同行驶状态下的车辆航向角估计结果
Figure 10. Vehicle heading estimation results under different driving state
表 1 算法定位误差均方根对比
Table 1. Position RMSE comparison of the algorithms
对比算法 北向RMSE/m 东向RMSE/m 地向RMSE/m 水平RMSE/m 3D RMSE/m 传统算法 2.1823 0.5544 3.4678 2.2516 4.1346 本文算法 2.0027 0.3397 2.4984 2.0313 3.2200 
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表 2 算法测速误差均方根对比
Table 2. Velocity RMSE comparison of the algorithms
对比算法 北向
RMSE/m东向
RMSE/m地向
RMSE/m水平
RMSE/m传统算法 0.1318 0.1153 0.0804 0.1751 本文算法 0.0841 0.0592 0.0385 0.1028
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表 3 算法定姿误差均方根对比
Table 3. Attitude RMSE comparison of the algorithms
对比算法 滚转RMSE/(°) 俯仰RMSE/(°) 航向RMSE/(°) 传统算法 0.5740 0.4538 0.3603 本文算法 0.5253 0.4225 0.1935
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