A novel integrated navigation approach with SRCKF based on LSTM signal compensation

In complex environments, the loss of Global Navigation Satellite System (GNSS) signals can lead to degraded performance or even failure of the GNSS/INS integrated navigation system. Therefore, a compensation method based on Rich Feature Long Short-Term Memory (RF-LSTM) is proposed to assist the GNSS/INS integrated navigation system. Firstly, a feature-rich dataset is constructed using the attitude, position and velocity data of inertial navigation system (INS) combined with the three-axis output data of gyroscope and accelerometer. Then, a time series neural network model based on LSTM is established. During the interruption of GNSS signals, this model can predict GNSS speed increment and position increment by using the input characteristics of the current and historical moments, so as to achieve the purpose of accurate compensation of GNSS signals, so that the integrated navigation system can operate normally without GNSS signals. Finally, through simulation experiments, the proposed method can compensate GNSS position and speed in real time when the GNSS signal is interrupted for as long as 30s, ensuring the continuous normal operation of the integrated navigation system. Compared with the traditional LSTM method, the average velocity measurement accuracy and positioning accuracy of this method are significantly improved. This research provides an effective solution to the navigation problem caused by GNSS signal interruption, and provides a new idea and direction for future research and application.