In a typical underwater acoustic target detection mission, we have to estimate the target number (N), perform source separation when $N>1$, and consequently predict the motion parameters such as fundamental frequency (F₀) from separated noises for each target. Although deep learning methods have been adopted in each task, their successes strongly depend on the feed-in features. In this paper, we evaluate several time-frequency features and propose a universal feature extraction strategy for object counting and F₀ estimation simultaneously, with a convolutional recurrent neural network (CRNN) as the backbone. On one hand, LOFAR and DEMON are feasible for low-speed and high-speed analysis, respectively, and are combined (LOFAR + DEMON) to cope with full-condition estimation. On the other hand, a comb filter (COMB) is designed and applied to the combined spectrum for harmonicity enhancement, which will be further streamed into the CRNN for prediction. Experiments show that (1) in the F₀ estimation task, feeding the filtered combined feature (LOFAR + DEMON + COMB) into the CRNN achieves an accuracy of 98% in the lake trial dataset, which is superior to LOFAR + COMB (83%) or DEMON + COMB (94%) alone, demonstrating that feature combination is plausible. (2) In a counting task, the prediction accuracy of the combined feature (LOFAR + DEMON, COMB included or excluded) is comparable to the state-of-the-art on simulation dataset and dominates the rest on the lake trial dataset, indicating that LOFAR + DEMON can be used as a common feature for both tasks. (3) The inclusion of COMB accelerates the convergence speed of the F₀ estimation task, however, it penalizes the counting task by a depression of 13% on average, partly due to the merging effects brought in by the broadband filtering of COMB.

Side‐scan sonar is essential for underwater observation and seabed target detection, yet traditional single‐frequency systems must compromise between imaging resolution and detection range. For object detection tasks, high‐frequency sonar yields finer details contributed by stronger scattering and higher contrast but suffers from limited coverage due to heavier attenuation, whereas low‐frequency sonar covers wider areas yet offers poorer small‐target visibility. To overcome these trade‐offs and the challenges of strong speckle noise, target–shadow coupling and resolution variation, this study proposes a dual‐frequency detection model named D ² FNet (dual‐domain fusion network). D ² FNet integrates three key modules: (1) a union domain attention (UDA) module for preliminary dual‐frequency fusion via ResNet‐50 and a transformer encoder; (2) a cross‐domain attention (CDA) module for enhanced feature interaction across frequency domains; and (3) a target–shadow pairing (TSP) module that embeds sonar imaging priors through local window attention to suppress false alarms and improve localisation confidence. Based on a newly constructed dual‐frequency side‐scan sonar dataset containing over 9000 paired images from sea trials, D ² FNet significantly outperforms single‐frequency and baseline fusion models in mAP metrics, demonstrating its effectiveness for high‐precision underwater target detection.

Propulsors are essential power units for underwater vehicles and major sources of self-noise, with unsteady pressure pulsations linked to flow-induced noise. To study the impact of guide vane case on the hydrodynamic performance and pressure pulsation of a shaftless pump-jet thruster, six guide vane configurations were first compared under a reference operating condition of 600 rpm: a vaneless baseline, fixed vertical guide vanes, fixed forward-inclined guide vanes, fixed reverse-inclined guide vanes, co-rotating guide vanes, and counter-rotating guide vanes. Based on this comparison, the vertical-vane and counter-rotating-vane configurations were selected for extended operating-condition analysis under three rotational speeds and three inflow conditions. Transient numerical simulations were conducted using the SST $k-\omega$ turbulence model. The rotating regions were defined in a rotating reference frame, and the unsteady rotor–stator interaction was resolved using a transient sliding-mesh interface. Results show that the counter-rotating guide vane configuration achieves the highest head and efficiency among the tested cases, with a head of 1.8955 m and efficiency of 0.7306, representing increases of 19.64% and 22.87% over the fixed vertical guide vane case. The fixed forward-inclined guide vane exhibits the strongest thrust fluctuation and pressure pulsation. Pressure-pulsation intensity generally decreases from the impeller rim toward the central axis. Frequency-domain results indicate that most cases are dominated by the blade-passing frequency, whereas the counter-rotating guide vanes show a response nearly twice this frequency.

Subsea Christmas trees are often deployed in turbid coastal waters or seabed environments. During manipulator operations on Christmas tree panels, conventional optical servoing is severely limited by rapid electromagnetic attenuation and strong scattering from suspended particles, resulting in reduced visibility. Forward-looking sonar (FLS) provides stable imaging, but its unique imaging geometry and low resolution make direct 6D pose estimation challenging. To address this issue, this paper proposes a 6D object pose estimation method for FLS images, in which conventional optical control-point-based pose estimation is restructured to resolve the mismatch between optical-centric network assumptions and acoustic imaging characteristics, and is further integrated with acoustic projection-based pose inversion. First, to address the limited diversity of target appearances and the scarcity of training data, we construct an FLS imaging model based on primary truncation for image simulation, providing data for model pretraining. Second, a multi-task acoustic control-point detection network, Acoustic-Yolo6D, is designed to mitigate localization degradation caused by heavy speckle noise, low boundary contrast, and resolution variations associated with polar-coordinate imaging, through heatmap regression, auxiliary object segmentation, and explicit range-bearing positional encoding. An Acoustic-n-Point (AnP) model is then used to recover the target 6D pose. Finally, simulation and water-tank experiments on the socket target verify the feasibility and robustness of the proposed method under limited-data conditions. The method achieves a 3.1 cm mean translation error, a 10.88° mean orientation error, and 52 FPS in real underwater acoustic environments.