基于多域标准化与类条件分布对齐的海面小目标雷达分类方法OA

Accurate classification of small sea-surface targets remains a critical technological challenge for advancing marine monitoring and defense security capabilities.In sea clutter environments,radar echoes from small surface targets are characterized by low signal strength and pronounced non-stationarity.Furthermore,distributional discrepancies across different radar systems limit the effectiveness of classification approaches based on single-domain features,which are unable to simultaneously capture radar cross-section variability,motion dynamics,and cross-platform generaliza-tion.To address these limitations,this study proposes a radar-based classification framework for small sea-surface targets that integrates multi-domain standardization with class-conditional distribution alignment.First,a multi-domain feature standardization preprocessing pipeline was developed to achieve consistent normalization of the target amplitude sequence,Doppler amplitude spectrum,and time-frequency representation.Subsequently,a three-branch parallel feature extraction network incorporating a Multi-Scale Dual Attention(MSDA)module was developed.The time-frequency branch employs a two-dimensional feature extraction architecture based on self-calibrated convolutions.In contrast,the amplitude sequence branch and the Doppler branch leverage the multi-scale dual attention module to capture local variations and long-range temporal dependencies,respectively.During the feature fusion stage,a class-conditional maximum mean discrepancy(CC-MMD)constraint was incorporated to align the cross-domain feature distributions of samples belonging to the same class within a shared latent space.This process effectively mitigates inter-class confusion,particularly for challenging targets such as floating spheres and boats.Experiments are conducted on the IPIX,CSIR,and measured UAV datasets.The results show that,compared with a single-branch network based exclusively on time-frequency features,the proposed method improves overall classification accuracy from 0.7665 to 0.8155.In addition,relative to the three-domain fusion network without class-conditional alignment,the accuracy was further increased by approximately 2.4%.These findings demonstrate that the proposed method significantly improves the classification performance of small sea-surface targets and provides robust discrimination for visually and statistically similar classes.