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 article proposes a deep-learning (DL) based method for accurate acoustic source localization in the deep ocean, which leverages a local three-dimensional (3D) acoustic intensity field (AIF) with dimensions of 1.26km×1.26km×504m to estimate the 3D position of a source within a range of 20km. The local AIF can be constructed from measurements from an underwater acoustic sensor network (UASN) using techniques such as optimal interpolation. To achieve accurate 3D localization, a 3D residual convolutional neural network (ResCNN) is designed for processing the AIF. Moreover, the method incorporates a 3D water column sound speed field (SSF) that covers the entire sound propagation path to enhance localization accuracy. To incorporate the environmental information for source localization, two approaches are introduced. One is to implicitly utilize the environmental information through network training based on training data generated in various environments. The second approach explicitly includes the 3D water column SSF to the network input. Since the environmental mismatching problem is inevitable, we discuss the impact of deviations between the adopted SSF and the real SSF on source localization accuracy. The training, validation, and test datasets are generated by the BELLHOP 3D acoustic simulation model. Once the proposed DL-based localization networks are fully trained, their performance outperforms that of a conventional matched-field processing (MFP) localization algorithm for the case where the mismatching factor of SSF obtained through common ocean model estimation ranges from 18% to 29%.

This paper addresses the aperture limitation problem faced by array-equipped underwater gliders (UGs) in direction-of-arrival (DOA) estimation. A passive synthetic aperture (PSA) method for DOA estimation using a single hydrophone mounted on a UG is proposed. This method uses the motion of the UG to synthesize a linear array whose elements are positioned to acquire the target signal, thereby increasing the array aperture. The dead-reckoning method is used to determine the underwater trajectory of the UG, and the UG’s trajectory was corrected by the UG motion parameters, from which the array shape was adjusted accordingly and the position of the array elements was corrected. Additionally, array distortion caused by movement offsets due to ocean currents underwent linearization, reducing computational complexity. To validate the proposed method, a sea trial was conducted in the South China Sea using the Haiyi 1000 UG equipped with a hydrophone, and its effectiveness was demonstrated through the processing of the collected data. The performance of DOA estimation prior to and following UG trajectory correction was compared to evaluate the impact of ocean currents on target DOA estimation accuracy.

Traversability analysis, previously developed for ground robots, is extended to be performed by a group of marine vehicles over ocean flow fields. To enable distributed path planning against flow, a systematic method is developed to determine a flow map that is significant for sharing among vehicles. Using traversability analysis, each vehicle finds a set of support vectors to approximate the boundary of flow regions with strong average flow speed. These support vectors, together with the parameters determined by the traversability analysis, are then shared over the communication links. The shared information is used by each vehicle to construct a flow map that supports path planning algorithms running on each vehicle. Underwater communication limits the amount of information contained in a map that can be shared among vehicles. A novel method for information reduction is developed so that only reduced flow maps are shared. The length of the data packets can be adjusted through an optimization process that balance between the fidelity of the approximated boundary and the number of bits that need to be transmitted. Simulation results demonstrate that the flow maps constructed from the shared information can have sufficient quality to allow near-optimal paths to be generated.

This paper addresses the aperture-limitation problem faced by array-equipped underwater gliders (UGs) in direction-of-arrival (DOA) estimation. A passive synthetic aperture (PSA) method for DOA estimation using a single hydrophone mounted on the UG is proposed. This method uses the motion of the UG to synthesize a linear array whose elements are positioned to acquire the target signal, thereby increasing the array aperture. The dead-reckoning method is used to determine the underwater trajectory of the UG, and the UG’s trajectory was corrected by the UG motion parameters, from which the array shape are adjusted accordingly and the position of the array elements was corrected. Additionally, array distortion caused by movement offsets due to ocean currents underwent linearization, reducing computational complexity. To validate the proposed method, a sea trial was conducted on 31 July 2018 in the South China Sea using the Haiyi 1000 UG equipped with a hydrophone, and its effectiveness was demonstrated through the processing of the collected data. The performance of DOA estimation prior to and following UG trajectory correction was compared to evaluate the impact of ocean currents on target DOA estimation accuracy.