Deep Gaussian Process Regression for Performance Improvement of POS During GPS Outages
Deep Gaussian Process Regression for Performance Improvement of POS During GPS Outages
复制标题
DOI：
10.1109/access.2020.3004706
复制
发表时间：
2020
期刊：
影响因子：
3.9
通讯作者：
Wen Ye;Bo Wang;Yanhong Liu;Bin Gu;Hongmei Chen
中科院分区：
文献类型：
--
作者：
Wen Ye;Bo Wang;Yanhong Liu;Bin Gu;Hongmei Chen
定位定向系统（POS）是一种高精度的惯性导航系统/全球导航卫星系统（INS/GNSS）组合系统，在GPS测量数据可用的情况下，利用卡尔曼滤波（KF）为机载遥感、移动的测绘和车辆定位等提供连续的时空参考。然而，在某些特殊的环境下，GPS定位系统会受到GPS中断的影响，其运动参数的精度会随着时间的积累而下降。为了抑制GPS中断引起的性能下降，这项工作提出了一种基于数据驱动的深度高斯过程回归（DGPR）的混合预测器，它使用多层高斯过程回归来处理高度复杂的数据关系和预测不确定性。一旦GPS中断发生，所提出的方法开始预测的观测测量，然后将其作为一个虚拟的更新，以估计所有的INS误差。通过真实的飞行试验验证了所提方法的有效性，实验结果表明，在各种GPS中断情况下，所提方法的性能得到了显著改善。
Position and orientation system (POS) is a high-precision inertial navigation systems/Global navigation satellite system (INS/GNSS) integrated system that can continuously provide time-spatial reference for airborne remote sensing, mobile mapping and vehicle localization using Kalman Filter (KF) during the availability of GPS measurements. However, the POS suffers from the GPS outages in some especial environment, whose accuracy of motion parameters degrades with the time accumulation. In order to suppress the performance degradation caused by GPS outages, this work proposes a hybrid predictor based on data-driven Deep Gaussian Process Regression (DGPR), which uses multi-layer Gaussian Process Regression to deal with highly complex data relationships and predictive uncertainty. Once GPS outages happen, the proposed approach starts to predict the observation measurement, and then feeds it to KF as a virtual update to estimate all the INS errors. The proposed approach is validated by the real flight test, and the experimental results show that significant performance improvement has been achieved during various GPS outages.