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

Abstract

Position and orientation system (POS) is a high-precision inertial navigation systems /Global navigation satellite system (INS/GNSS) integrated system that can continuously provide timespatial 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.

Online State-of-Health Estimation for Second-Use Lithium-Ion Batteries Based on Weighted Least Squares Support Vector Machine

“…A BPNN [28] is a multilayer feedforward neural network in an artificial neural network, which is the most representative and extensive approach. The GPR [29], [34]- [36] is designed based on the Bayesian framework [37], [38] and is widely used in prediction tasks [39]- [41]. In SOH estimation, the inputs for the BPNNbased method, the GPR-based method, and the standard SVM-based method are also HF3 and HF4.…”

“…where ( , ) is the kernel function. Due to its good ability to deal with nonlinear problems, the radial basis kernel is one of the most widely used functions [34]; it can be written as:…”

A novel hybrid observation prediction methodology for bridging GNSS outages in INS/GNSS systems

“…(2014) and Ye et al. (2018a, 2018b, 2020) suggested applying the Gaussian process regression (GPR) technique to cope with observation outages and restrain the INS error accumulation during GNSS signal failures. GPR has good applicability with strong generalisation capacity to resolve the high dimension, small sample size and nonlinear characteristic problems.…”

“…Nonetheless, SVM is of higher complexity arithmetic, and its kernel function selection and parameter adjustment has great influence on the prediction accuracy. Atia et al (2011 and Ye et al (2018aYe et al ( , 2018bYe et al ( , 2020 suggested applying the Gaussian process regression (GPR) technique to cope with observation outages and restrain the INS error accumulation during GNSS signal failures. GPR has good applicability with strong generalisation capacity to resolve the high dimension, small sample size and nonlinear characteristic problems.…”