面向电磁大数据的未知雷达辐射源智能识别

Intelligent recognition of unknown radar emitters for electromagnetic big data

摘要:

基于人工智能的方法在雷达辐射源识别任务中已取得很好的效果。但随着电子信息技术的发展，将会出现越来越多的未知辐射源，其特征分布与类别都是未知的，在缺少先验知识的情况下，难以对人工智能模型进行充分的训练，使得现有的大多数方法都无法很好地完成对未知雷达辐射源的识别。为了解决上述问题，提出并阐述了可用于未知雷达辐射源识别的电磁大数据的解决方案，重点研究了基于Flink的未知雷达辐射源快速比对检索识别算法。通过对比实验证明了该方法的有效性，其识别准确率可达87.2%，当并行度设置为6时，整个互信息与K最近邻(MI-KNN)并行化算法耗时仅为4.7 s。

Abstract:

At present, artificial intelligence-based methods have been able to achieve good results in radar emitter recognition task. However, with the development of electronic information technology, there will be more and more unknown emitters whose characteristic distribution and categories are unknown. In the absence of prior knowledge, it is difficult to fully train the artificial intelligence model, which makes most of the existing methods unable to well complete the recognition of unknown radar emitters. This paper proposes a big electromagnetic data solution that can be used for the recognition of unknown radar emitters, and then focuses on the Flink-based fast comparison retrieval and recognition algorithm for unknown radar emitters. Finally, a comparative experiment proves the effectiveness of the proposed method, and its recognition accuracy can reach 87.2%. When the parallelism is set to 6, the entire Mutual Information- K-Nearest Neighbor(MI-KNN) parallelization algorithm takes only 4.7 s.