Deep Gaussian Process Regression for Performance Improvement of POS During GPS Outages

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.