发表机构
University of Electronic Science and Technology of China; Tianfu Jiangxi Laboratory（电子科技大学; 天府江西实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对雷达目标检测中部分错误标注训练数据导致性能下降的问题，提出鲁棒学习框架SL-RLF，通过满足对称性条件的损失函数实现鲁棒性，理论证明其性能与准确标注时相当，实验验证优于现有方法。
AI 中文摘要
基于深度学习的雷达目标检测显著提升了检测性能，但其有效性关键取决于分配给雷达回波样本的训练标签的正确性。在实际应用中，训练数据可能包含部分错误标注的样本，这会导致检测模型学习到有偏的监督信息，从而降低检测性能。为解决这一问题，本文提出SL-RLF，一种针对部分错误标注训练数据的基于深度学习的雷达目标检测鲁棒学习框架。具体而言，我们建立了一个概率模型来刻画雷达目标检测中标签错误的生成机制，并在风险最小化框架下分析了部分标签错误下检测模型的鲁棒性。理论分析表明，在适当假设下，满足对称性条件的损失函数能使学习框架中的检测模型对部分标签错误具有鲁棒性。基于这一结果，我们设计了一个满足对称性条件的损失函数，并构建了所提出的鲁棒学习框架SL-RLF。理论上，在相应的标签错误条件下，SL-RLF从部分错误标注的训练数据中学到的最优检测模型，在真实数据分布下能达到与从准确标注数据中学到的最优模型相同的检测性能。实验结果表明，在不同类型和水平的部分标签错误下，所提出的SL-RLF始终优于代表性方法（包括Co-teaching和TCE），其鲁棒性与理论分析基本一致。
英文摘要
Deep-learning-based radar target detection has substantially improved detection performance, but its effectiveness depends critically on the correctness of the training labels assigned to radar echo samples. In practical applications, training data may contain partially mislabeled samples, which can cause detection models to learn biased supervisory information and thereby degrade detection performance. To address this issue, this paper proposes SL-RLF, a robust learning framework for deep-learning-based radar target detection with partially mislabeled training data. Specifically, we establish a probabilistic model to characterize the generation mechanisms of label errors in radar target detection and analyze the robustness of detection models under partial label errors within a risk minimization formulation. Theoretical analysis shows that, under appropriate assumptions, a loss function satisfying the symmetry condition enables the detection model in the learning framework to be robust against partial label errors. Guided by this result, we design a loss function satisfying the symmetry condition and construct the proposed robust learning framework, SL-RLF. Theoretically, under the corresponding label-error conditions, the optimal detection model learned by SL-RLF from partially mislabeled training data can achieve the same detection performance under the true data distribution as the optimal model learned from accurately labeled data. Experimental results demonstrate that, under different types and levels of partial label errors, the proposed SL-RLF consistently outperforms representative methods, including Co-teaching and TCE, and its robustness is generally consistent with the theoretical analysis.
