TL-ESKF: An information fusion method for INS/GPS integrated navigation considering driving state deviation

Abstract

When the global positioning system (GPS) signal is unavailable, the positioning performance of the GPS/inertial navigation system (INS) integrated navigation system significantly degrades, leading to deviation in the vehicle driving state. To address the limitations of information fusion during GPS outages and enhance navigation performance, this paper proposes an information fusion method based on transfer-learning error-state Kalman filter (TL-ESKF). This method consists of two steps, each combining a long short-term memory (LSTM) network with an ESKF. First, the dependence of the vehicle’s current position on historical INS and GPS data is considered, and the relationship between the gain of ESKF-1 and the observation vectors is established through the LSTM, providing an initial correction for the vehicle driving state deviation. Then, a second LSTM-ESKF combination further refines the correction, with transfer learning employed to expedite the training process of the TL-LSTM. Finally, the effectiveness of the proposed method is evaluated using both public datasets and real field tests. The experimental results indicate that, during GPS signal outages, the proposed method delivers more accurate navigation solutions. In comparison to high-precision machine learning methods, it offers a significantly higher training efficiency.