TCN-Attention-Based Loosely Coupled SINS/DVL Integrated Navigation Method

Doppler velocity log(DVL) unavailability in deep-water or complex seafloor environments degrades the accuracy of loosely coupled strapdown inertial navigation system(SINS)/DVL integrated navigation for autonomous undersea 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 calculations, as sequential inputs. During the DVL-available stage, supervised samples are constructed using DVL velocity measurements, and the network is trained offline. 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 navigation 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 sets of trajectory data show that, compared with the temporal convolutional network(TCN) and the gated recurrent unit with attention model(GRU-Attention), the proposed method achieves lower eastward and northward velocity errors as well as lower absolute trajectory error, and reconstructs trajectories closer to the ground truth, demonstrating its effectiveness and robustness under continuous DVL-outage conditions.