Underwater acoustic target recognition (UATR) is challenging due to the complex, multi-scale physical characteristics of marine targets and the strict computational limits of edge platforms like unmanned surface vehicles. To navigate the severe interference of underwater environments, existing methods increasingly rely on heavyweight architectures to achieve high recognition accuracy. However, the massive computational overhead of these models is fundamentally at odds with the restricted power and processing capabilities of practical deployment platforms. To resolve this conflict between performance and deployability, we propose LHK-Net, a lightweight Heterogeneous Kernel Network. By integrating a Heterogeneous Kernel Pyramid with Residual Depthwise Separable Convolutions, LHK-Net dynamically captures multi-scale acoustic features, from macroscopic steady-state harmonics to localized transient impulses, while compressing the model size to merely 0.82 M parameters. Additionally, a dual-domain Time–Frequency Attention module and an Adaptive SK-Fusion mechanism are incorporated for robust noise suppression. Experiments on the DeepShip dataset demonstrate that LHK-Net achieves state-of-the-art accuracy, outperforming heavyweight models at real-time speeds. Extensive visual analyses further validate that the network possesses strong physical interpretability, effectively aligning its internal feature representations with the intrinsic acoustic properties of the targets.

The complex seabed topography and mechanical properties of deep-sea sediments impose stringent requirements on the traction performance and locomotion stability of tracked mining vehicles. Experimental investigations on the coupled effects of grouser geometry and operating conditions on traction remain limited. To address this, rheological tests and multi-parameter traction experiments were conducted. Deep-sea sediments were modeled as a power-law fluid to capture their non-Newtonian behavior, considering particle size distribution, water content, and compaction state. Using a self-designed traction test apparatus, the influences of grouser geometry and operating parameters on traction force were systematically analyzed. Results indicate that both grouser configuration and operating conditions significantly affect traction force magnitude and stability. Rectangular grousers, exhibiting more uniform stress distribution and pronounced shear bands, demonstrated enhanced traction efficiency and locomotion stability under high-load, low-speed conditions. When the grouser length was 30 mm and the traveling speed was maintained at 7–12 mm/s, sediment fluidization was significantly mitigated, improving traction performance. Furthermore, a spacing of at least 20 mm between adjacent grousers produced a synergistic effect, increasing sediment shear strength by approximately 30–40%. These findings provide quantitative guidance for grouser design and operational optimization of tracked deep-sea mining vehicles.