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In light of the prevailing approach in which data from side-scan sonar (SSS) from Autonomous Underwater Vehicles (AUVs) are primarily processed and visualized post mission, failing to meet the requirements in terms of timeliness for on-the-fly image acquisition, this paper introduces a novel method for real-time processing and superior imaging of navigation strip data from SSS aboard AUVs. Initially, a comprehensive description of the real-time processing sequence is provided, encompassing the integration of multi-source navigation data using Kalman filtering, and high-pass filtering of attitude and heading data to exclude anomalies, as well as the use of bidirectional filtering techniques within and between pings, ensuring real-time quality control of raw data. In addition, this study adopts the semantic segmentation Unet network for automatic real-time tracking of seafloor lines, devises a real-time correction strategy for radial distortion based on historical echo data, and utilizes the alternating direction multiplier method for real-time noise reduction in strip images. With the combined application of these four pivotal techniques, we adeptly address the primary challenges in real-time navigation data processing. In conclusion, marine tests conducted in Bohai Bay substantiate the efficacy of the methodologies delineated in this research, offering a fresh paradigm for real-time processing and superior visualization of SSS navigation strip data on AUVs. [ABSTRACT FROM AUTHOR]

The limitations of underwater acoustic communications mean that the side-scan sonar data of an autonomous underwater vehicle (AUV) cannot be transmitted back and processed in real time, which means that targets cannot be detected in real time. To address the problem, this paper proposes an autonomous underwater vehicle-based side-scan sonar real-time detection method for underwater targets. First, the paper describes the system and operation of real-time underwater-target detection by the side-scan sonar mounted on the autonomous underwater vehicle. Next, it proposes a real-time processing method for side-scan sonar data, method for constructing a deep-learning-based underwater-target detection model, and real-time method for underwater-target detection based on navigation strip images, which, together, solve the three key technical problems of real-time data processing, deep-learning-based detection model construction, and real-time target detection based on the autonomous underwater vehicle. Finally, through sea-based experiments, the effectiveness of the proposed methods is evaluated, providing a new solution for the autonomous underwater vehicle-based side-scan sonar real-time detection of underwater targets. [ABSTRACT FROM AUTHOR]

Aiming at the problem that lack of the measured sound velocity profile (SVP) leads to the unreliable underwater positioning solution, this paper proposed an efficient underwater positioning method by the self-constraint conditions of water depth and sound velocity gradient. To construct the depth constraint condition, the sound propagation distance error model is deduced by acoustic ray tracing, and the sound vertical propagation error model related to the incident angle and sound velocity error is given firstly. By fitting the vertical propagation error model, the reference depth is solved, and the vertical propagation distances between the transducer and the underwater control points of all observation epochs are gotten. Then with the solved vertical distance of each epoch and the sound velocity gradient from neighbor SVPs as the constraint conditions, the SVP is retrieved by the simulated annealing (SA) algorithm. With the retrieved SVP, the underwater positioning can be performed when the measured SVP is absent. The proposed method was verified by an experiment in the 3000 m depth water area of the South China Sea. The results achieved 2.07 m/s of standard deviation of the SVP inversion, centimeter-level horizontal positioning accuracy and 0.54 m of vertical positioning accuracy under the circumstance of lack of SVP measurement. [ABSTRACT FROM AUTHOR]