Abstract
In underwater target recognition, the scattering characteristics of small targets are weak and highly sensitive to observation angles, posing significant challenges to achieving stable and robust recognition in complex environments. Existing methods are mainly data-driven and rely on closed-set classifiers, which often lack physical interpretability and show limited generalization under different observation conditions. To address these issues, a physics-guided deep embedding framework for underwater target recognition is proposed. Firstly, an encoder–decoder network is designed to learn representative and physically consistent scattering features from measured echo frequency spectra. The encoder is then extracted to construct a Triplet-based embedding model, which maps high-dimensional scattering spectra into a discriminative low-dimensional feature space. In the embedding space, a similarity-based decision strategy is further adopted to replace the traditional classifier, and recognition is achieved by evaluating the relationships among embedded features. Experimental results show that the proposed method achieves robust recognition performance under varying observation angles and establishes an interpretable connection between scattering characteristics and recognition results. The proposed framework provides an effective way to combine physics-guided feature learning with deep embedding methods for underwater target recognition.