Physics and metadata-guided generative learning for underwater acoustic target recognition

Underwater acoustic target recognition (UATR) is challenged by data scarcity and long-tailed class distributions, where limited recordings provide incomplete coverage of vessel attributes and recording conditions. We propose a physics and metadata-guided generative learning (PMGL) framework comprising physics and metadata-guided acoustic generation (PMAG), physical semantic coverage expansion (PSCE), and physical semantic distribution rebalancing (PSDR). PMAG constructs structured physical semantic conditions from metadata and environmental priors to guide text-to-audio generation. PSCE expands intraclass condition coverage for few-shot UATR, while PSDR supplements underrepresented categories to construct a more balanced training distribution for long-tailed UATR. Experiments on the ShipsEar and DeepShip datasets demonstrate consistent improvements in recognition performance over the original training setting and a baseline based on a generative adversarial network (GAN), particularly under data-scarce and distribution-imbalanced conditions. The results indicate that the proposed PMGL framework effectively compensates for structural data deficiencies in underwater acoustic recognition.