基于多模特征融合的雷达干扰信号识别

传统制导雷达面临的新型有源干扰样式越来越复杂，雷达必须对各种干扰类型加以鉴别。传统的干扰识别方法仅对特定单一样式有效，通用性较差、泛化能力较弱，无法应对复杂多变的干扰对抗环境。因此，必须提出智能化更高、稳健性更强的普适干扰识别方法，提升制导武器抗干扰能力。为了提高干扰信号识别的准确率，研究了多模特征融合算法，并最终对时域、时频域、信息论特征进行融合以实现分类。首次将信息论中熵、相对熵、相对距离等概念引入到干扰信号分类这个应用场景中，通过仿真实验表明，能够有效对常见干扰进行有效识别，在较低干噪比下也有较好的识别准确率。

Radar jamming signal identification based on multimode feature fusion

The traditional guidance radar is facing increasingly complex new types of active interference patterns, and the radar must discriminate against various types of interference. The traditional interference recognition method is only effective for a specific single pattern, which has the disadvantages of poor generality and weak generalization ability, resulting in its inability to cope with the complex and changing interference countermeasures. Therefore, it is necessary to propose a more intelligent and robust universal interference recognition method to enhance the anti-interference ability of guided weapons. In order to improve the accuracy of interference signal recognition, this paper studied the multi-mode feature fusion algorithm, and finally fused time-domain, time-frequency domain, and information theory features to achieve classification. This paper introduces for the first time the concepts of entropy, relative entropy, and relative distance in information theory into the application scenario of interference signal classification. Through simulation experiments, it is shown that this method can effectively identify common interference and has good recognition accuracy even at low jam-to-noise ratios.