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.

中文翻译：

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基于敏捷频率 RCS 的深度融合网络，用于船舶和角反射器识别

在雷达目标识别（RTR）中，反角反射器干扰是一个关键的研究领域。雷达截面 (RCS) 是常用的雷达数据，用于识别船舶和角反射器。然而，考虑到目前的情况，基于RCS的船舶识别严重依赖于单频多角度数据，这限制了其潜力。在分类方法方面，手动特征提取进行分类存在主观性强、工作量大、适应性有限等缺点。直接使用卷积神经网络（CNN）也存在局限性，包括数据依赖性和性能上限问题。为了应对这些挑战，我们提出了一种在敏捷频率条件下使用 RCS 进行船舶和角反射器识别的特征融合方法。我们引入了一种基于通道注意机制的自动加权模块，用于手动提取的可解释特征。这些加权可解释特征与改进的全尺度 CNN (OS-CNN) 的深层特征相结合。实验表明，该方法可以有效地区分船舶和角反射器，减少训练过程中对观察角度的依赖。测试集上的整体识别准确率达到96.2%，高于现有方法3.4%~10.6%，并且对不同海况的波动具有鲁棒性。