부산대학교 도서관

In radar target recognition (RTR), anticorner reflector interference is a critical research area. Radar cross section (RCS), commonly used radar data, serves to recognize ship and corner reflectors. However, considering the current circumstances, RCS-based ship recognition heavily relies on single-frequency multiangle data, which limits its potential. In terms of classification methods, manual feature extraction for classification has drawbacks like subjectivity, high workload, and limited adaptability. Direct use of convolutional neural networks (CNNs) also presents limitations, including data dependency and the problem of performance upper bounds. To address these challenges, we propose a feature fusion approach for ship and corner reflector recognition using RCS under agile frequency conditions. We introduce an automatic weighting module based on a channel attention mechanism for interpretable features extracted manually. These weighted interpretable features are combined with deep features from the improved Omni-Scale CNNs (OS-CNN). The experiment shows that the proposed method effectively discriminates between ships and corner reflectors and reduces reliance on observation angles during training. The overall recognition accuracy on the test set reaches 96.2%, higher than the existing methods of 3.4%~10.6%, and is robust to the fluctuation of varying sea conditions.