INS/WT-DVL Integrated Navigation Algorithm Based on Improved Expected-Mode Augmentation for Underwater Vehicles

For underwater vehicles, the measurement accuracy of Doppler velocity log (DVL) working in water-track (WT) mode is affected by the ocean current, thereby affecting the performance of the inertial navigation system (INS)/WT-DVL integrated navigation system. As for the uncertainty of the ocean-current velocity model, we propose a novel integrated navigation algorithm based on an improved expected-mode augmentation (IEMA) algorithm, which is used to mitigate the effects of complicated ocean currents. First, we set up the basic fixed model set. Then, use the model set augmentation strategy to generate an expected model set that is more compatible with the actual ocean-current velocity model. Finally, use the expected model set to modify the estimation results of the basic fixed model set to acquire more precise ocean-current velocity data. In the proposed IEMA algorithm, a new model set augmentation strategy is designed by improving the existing expected-mode augmentation (EMA) algorithm. First, not only use the predicted probability, but also need to further use the measurement information at the current time to generate the expected model set. Second, multiple ocean-current velocity models (only one model in the existing EMA algorithm) are selected to form an expected model set, as a way to increase the proportion of expected models in the entire model set. Compared with the existing EMA algorithm, the IEMA algorithm not only has a smaller steady-state error but also substantially reduces the peak error. Simulated and experimental results indicate that the proposed IEMA algorithm can accurately estimate and compensate the ocean-current velocity more efficiently, and further improve environmental adaptability and enhance navigation accuracy.