An accurate motion model and reliable measurements are required for autonomous underwater vehicle localization and navigation in underwater environments. However, without a propeller, underwater gliders have limited maneuverability and carrying capacity, which brings difficulties for modeling and measuring. In this paper, an extended Kalman filter (EKF)-based method, combining a modified kinematic model of underwater gliders with the travel-time differences between signals received from a single beacon, is proposed for estimating the glider positions in a predict-update cycle. First, to accurately establish a motion model for underwater gliders moving in the ocean, we introduce two modification parameters, the attack and drift angles, into a kinematic model of underwater gliders, along with depth-averaged current velocities. The attack and drift angles are calculated based on the coefficients of hydrodynamic forces and the sensor-measured angle variation over time. Then, instead of satisfying synchronization requirements, the travel-time differences between signals received from a single beacon, multiplied by the sound speed, are taken as the measurements. To further reduce the EKF estimation error, the Rauch-Tung-Striebel (RTS) smoothing method is merged into the EKF system. The proposed method is tested in a virtual spatiotemporal environment from an ocean model. The experimental results show that the performance of the RTS-EKF estimate is improved when compared with the motion model estimate, especially by 46% at the inflection point, at least in the particular study developed in this article.

Using underwater gliders to measure underwater acoustic signals is a new measurement method emerging with the development of platform technology. Although underwater gliders are relatively quiet due to the absence of propellers, vehicle noise generated during motion is still inevitable, which will affect the recorded acoustic data. In this paper, we analyze the self-noise characteristics of underwater gliders based on simulated data by CFD technology for the hydrodynamic flow noise and experimental data acquired in an anechoic-water-tank experiment for the mechanical noise. The mechanical noise covers noises generated by buoyancy regulating (increasing and reducing), pitch regulating, rudder regulating and CTD pump working. According to the analysis results, the flow noise and CTD pump working noise could be ignored for the experimental data processing of sea trials. An experiment was conducted with an acoustic Sea-Wing underwater glider in the South China Sea from July 31 to September 4, 2018. Two kinds of noisy data were recorded, including target signals and ambient noise. All the target signals could be recognized after convolution filtering, except during the buoyancy regulating periods due to the high noise spectrum level. For the recorded ambient noise, in addition to the buoyancy regulating noise, the rudder and pitch regulating noises affected the recorded data. Then based on the acquired knowledge, a joint convolution filtering and thresholding method is proposed to remove the rudder and pitch noises from recorded noisy data. Kernels extracted from data acquired in the anechoic-water-tank experiment are used in the convolution filtering to localize each regulating action and energy thresholding is adopted to determine the duration of each regulation. All the rudder and pitch noises are removed from the recorded noisy data.

This study investigates the tracking of underwater cables using autonomous underwater vehicles (AUVs) equipped with side-scan sonar (SSS). AUV motion stability is crucial for effective SSS imaging, which is essential for continuous cable tracking. Traditional methods that derive AUV guidance rates directly from measured cable states often cause unnecessary jitter when imaging, complicating accurate detection. To address this, we propose a non-myopic receding-horizon optimization (RHO) strategy designed to maximize cable imaging quality while considering AUV maneuvering constraints. This strategy identifies the optimal heading decision sequence over a future horizon, ensuring stable and efficient cable tracking. We also employ a long short-term memory (LSTM) network to predict future cable states, further minimizing AUV motion instability during abrupt path changes. Given the computational limitations of AUVs, we have developed an efficient decision-making framework that can execute resource-intensive algorithms in real time. Finally, the robustness and effectiveness of the proposed algorithm were validated through comparative experiments. The results demonstrate that the proposed method outperforms existing methods in key metrics such as cable-tracking accuracy and AUV motion stability. This ensures that the AUV can acquire high-quality acoustic images of the submarine cable in an optimal state, enhancing the continuity and reliability of cable-tracking tasks.