A TCN-Attention-Based Pseudo-Velocity Measurement Generation Method for Loosely Coupled SINS/DVL Integrated Navigation

DVL unavailability degrades the accuracy of loosely coupled SINS/DVL integrated navigation for autonomous underwater vehicles. To address this problem, a pseudo-DVL velocity measurement generation method based on a temporal convolutional network with an attention mechanism (TCN-Attention) is proposed. The method uses the angular velocity and specific force measured by the inertial measurement unit (IMU), together with the attitude, position, and velocity obtained from inertial navigation computation, as sequential inputs. During the DVL-available stage, supervised samples are constructed using DVL velocity for offline network training. During the DVL-unavailable stage, the trained model outputs pseudo-velocity measurements, which are incorporated into the extended Kalman filter (EKF) update to suppress inertial error accumulation. Causal dilated convolutions are adopted to extract temporal features, and an attention mechanism is introduced to enhance the representation of key dynamic segments such as turning, acceleration, and deceleration. Simulation results based on 16 trajectory datasets show that, compared with the temporal convolutional network (TCN) and the gated recurrent unit with attention model (GRU-Attention), the proposed method achieves better performance in east- and north-velocity errors as well as absolute trajectory error, and reconstructs trajectories closer to the ground truth, demonstrating its effectiveness and robustness under continuous DVL-outage conditions.