Learning from Noisy Label for HRRP Signal Recognition
Xu Si, Peikun Zhu, Jing Liang · 2024
Supervised machine learning technology has greatly improved the accuracy of radar target recognition based on HRRP signals. It relies on complete dataset labels, but the dataset is prone to noise labels due to the instability of data collection and the abstract nature of the HRRP signal itself. This problem will affect the model training robustness and testing accuracy. In this paper, we propose a noisy label learning method, "Contrastive Learning with or without Freeze, CLwF" to solve this problem. CLwF proposes a self-supervised learning algorithm to train models for efficient representation of HRRP signals. We also propose a clean rate estimation module to select the correct fine-tuning strategy for the model training with noisy labels. The experiment verified that CLwF can achieve excellent results under different noise rates.