Gaussian process regression approach for bridging GPS outages in integrated navigation systems

Abstract

A Kalman filter (KF) enhanced by the Gaussian process regression (GPR) technique is suggested to bridge GPS-outages in navigation solutions where inertial navigation systems (INS) and GPS are integrated. A KF utilises linearised dynamic models. If a low-cost MEMS-based INS with complex stochastic nonlinearity is considered, performance degrades significantly during short periods of GPS-outages owing to linearised models. Proposed is a novel usage of GPR as a nonlinear INS-errors predictor. During GPS availability, the correct vehicle state, sensor measurements, and INS output deviations from GPS are collected. During GPS-outages, GPR is applied to this data set to predict INS deviations enabling the KF to estimate all INS errors. The proposed technique was tested on real road experiments showing significant improvements during long GPS-outages.