INS-Aided GNSS Pseudo-Range Error Prediction Using Machine Learning for Urban Vehicle Navigation

Global navigation satellite system (GNSS) is being extensively applied in different navigation applications. However, GNSS direct signals are easily affected by multipath and non-line-of-sight (NLOS) signals, resulting in severe deterioration of positioning. GNSS receiver output information, such as carrier-to-noise ratio (C/N0) and satellite elevation, cannot accurately reflect the pseudo-range quality, leading to a significant increase in positioning errors. This article proposes an inertial navigation system (INS)-aided GNSS pseudo-range error prediction approach based on machine learning for urban vehicle navigation. As an important feature, the pseudo-range residual estimated by INS is employed for model training, together with the C/N0, satellite elevation, and pseudo-range rate consistency. The predicted model of the pseudo-range errors is obtained by an ensemble bagging decision tree learning method. Urban vehicle tests show that compared to GNSS single-point positioning (SPP) with C/N0-based weighting, the horizontal accuracy in the form of CEP95 of SPP with model-based weighting improves 52.81%, and the GNSS/INS horizontal positioning error in the form of CEP95 is reduced from 21.23 to 5.02 m in deep urban environments.