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首页> 外文期刊>Survey Review >IMPROVING POSITIONING ACCURACY DURING KINEMATIC DGPS OUTAGE PERIODS USING SINS/DGPS INTEGRATION AND SINS DATA DE-NOISING
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IMPROVING POSITIONING ACCURACY DURING KINEMATIC DGPS OUTAGE PERIODS USING SINS/DGPS INTEGRATION AND SINS DATA DE-NOISING

机译：使用SINS / DGPS集成和SINS数据去噪可提高运动DGPS中断期间的定位精度

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摘要

In the standard integration of a Differential Global Positioning System (DGPS) and a Strapdown Inertial Navigation System (SINS), the DGPS provides position information while the SINS provides attitude information. In addition, the DGPS measurements are used to estimate the inertial sensors systematic errors and the SINS is used to detect and correct GPS cycle slips. In case of GPS signal blockages, the SINS is used instead for positioning as a stand-alone system until the GPS signals are available again. To obtain accurate positions during DGPS outages, near real-time (or post-mission) techniques should be applied, where these techniques are known as bridging algorithms. In such algorithms, new and improved positions of the outage periods are estimated. In this paper, two different bridging methods are used namely: backward smoothing and parametric modeling. An SINS/DGPS data collected with a van has been used in the analysis. The results show that both bridging algorithms reduce the SINS positional errors for DGPS outages of 75 to 100 seconds with an average of 1.35 m to an RMSE of 19 cm in case of backward smoothing and 10 cm in case of parametric modeling. To separate between the actual motion dynamics and other disturbing vibrations, a de-noising of the SINS raw data is required. Therefore, a de-noising of the van SINS data has been applied using a wavelet decomposition technique to eliminate or minimize the effect of sensor noise and other high frequency disturbances (such as engine vibrations). An analysis of the SINS sensor kinematic raw data in the frequency domain shows clearly that the majority of the van motion dynamics are contained in the low frequency portion of the spectrum (below 3.0 Hz). Consequently, several levels of wavelet decomposition can be performed without losing any motion information. The application of both bridging methods after the SINS data de-noising reduces the positional RMSE to 11 cm and 7.7 cm using backward smoothing and parametric modeling, respectively.
机译：在差分全球定位系统（DGPS）和捷联惯性导航系统（SINS）的标准集成中，DGPS提供位置信息，而SINS提供姿态信息。此外，DGPS测量值用于估算惯性传感器的系统误差，而SINS用于检测和校正GPS周期滑移。如果GPS信号阻塞，则使用SINS作为独立系统进行定位，直到GPS信号再次可用为止。为了在DGPS中断期间获得准确的位置，应采用近实时（或任务后）技术，这些技术被称为桥接算法。在这样的算法中，估计中断时间段的新位置和改进位置。在本文中，使用了两种不同的桥接方法：反向平滑和参数化建模。分析中使用了用货车收集的SINS / DGPS数据。结果表明，两种桥接算法均可以将DGPS中断的SINS位置误差减少75至100秒，平均向后平滑时为1.35 m，RMSE为19 cm，而参数化建模时为10 cm。为了区分实际的运动动力学和其他干扰振动，需要对SINS原始数据进行消噪。因此，已经使用小波分解技术对van SINS数据进行了消噪，以消除或最小化传感器噪声和其他高频干扰（例如发动机振动）的影响。对SINS传感器运动学原始数据在频域中的分析清楚地表明，大多数货车运动动力学包含在频谱的低频部分（低于3.0 Hz）中。因此，可以执行几个级别的小波分解而不会丢失任何运动信息。在SINS数据去噪之后，两种桥接方法的应用分别使用反向平滑和参数化建模将位置RMSE分别减小到11 cm和7.7 cm。

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