Anti-Chaff Jamming Method of Radar Based on Real Dataset and Residual Attention Model

As a typical and widely used passive jamming method, chaff clouds have a strong interference effect on radar that remains a significant challenge effectively to counteract. It is exceedingly necessary to improve the anti-chaff jamming ability of radars. In this paper, we address this challenge by proposing an effective residual attention network named RA-Net. Specifically, we introduce an attention mechanism that enables the network to focus on the most informative and stable hierarchical features of the high-resolution range profile (HRRP) data, significantly improving the model's feature extraction capability and overall performance. In addition, we address the limitation of insufficient measured chaff cloud echo data by establishing a remarkably rich and diverse data set of chaff cloud HRRP data through extensive field experiments. This dataset serves as a valuable resource and a critical foundation for advancing HRRP recognition research in this domain. Experimental results on measured HRRP data demonstrate that RA-Net achieves superior recognition accuracy of 97.10%, outperforming traditional methods, and also exhibits outstanding generalization capability. These results establish RA-Net as a new benchmark for chaff cloud HRRP recognition.