A Robust Learning Framework for Deep-Learning-Based Radar Target Detection With Partially Mislabeled Training Data
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.