An INS/DVL/PS Integrated Underwater System Based on NLS-Transformer-LSTM Velocity Prediction Model

High-precision underwater navigation is a prerequisite for underwater vehicles to explore the marine environment and resources. Inertial navigation system (INS), Doppler velocity logger (DVL) and Pressure sensor (PS) onboard the underwater vehicles are favored for integrate due to their effectiveness and user-friendly configuration. Specifically, the velocity measurement from the DVL is important to reduce the accumulated error of the INS. Different from the traditional filter-based fusion solution, we firstly introduce a Factor graph optimization (FGO) framework to integrate the INS/DVL/PS navigation system. Since the DVL observations often experience outliers and outage due to the harsh sea condition and range limitation, it can lead to rapid position drift and error accumulation in the navigation system. Thus, we develop an outlier detection scheme based on the improved interquartile range method, which combines sliding window and dynamic threshold adjustment of the propeller revolutions per minute (RPM). Then, we propose a DVL velocity prediction method based on the nonlinear least squares (NLS)-Transformer-LSTM model. We refine the RPM by NLS and use it as one of the key features in the prediction model. Finally, the constructed system is compared with several classical Kalman filters through both simulated and measured experiments. The performance of the NLS-Transformer-LSTM model is comprehensively evaluated. The results indicate that the system can provide more accurate pseudo-DVL velocity estimates. This capability ensures more stable and reliable underwater navigation accuracy during DVL outages. Consequently, it offers strong support for autonomous underwater vehicle applications in complex marine environments.