Dudczyk Janusz, Rybak Łukasz
Institute of Telecommunications Systems, Faculty of Electronics, Military University of Technology, 00-908 Warsaw, Poland.
Sensors (Basel). 2023 Sep 30;23(19):8183. doi: 10.3390/s23198183.
The process of recognising and classifying radar signals and their radiation sources is currently a key element of operational activities in the electromagnetic environment. Systems of this type, called ELINT class systems, are passive solutions that detect, process, and analyse radio-electronic signals, providing distinctive information on the identified emission source in the final stage of data processing. The data processing in the mentioned types of systems is a very sophisticated issue and is based on advanced machine learning algorithms, artificial neural networks, fractal analysis, intra-pulse analysis, unintentional out-of-band emission analysis, and hybrids of these methods. Currently, there is no optimal method that would allow for the unambiguous identification of particular copies of the same type of radar emission source. This article constitutes an attempt to analyse radar signals generated by six radars of the same type under comparable measurement conditions for all six cases. The concept of the SEI module for the ELINT system was proposed in this paper. The main aim was to perform an advanced analysis, the purpose of which was to identify particular copies of those radars. Pioneering in this research is the application of the author's algorithm for the data particle geometrical divide, which at the moment has no reference in international publication reports. The research revealed that applying the data particle geometrical divide algorithms to the SEI process concerning six copies of the same radar type allows for almost three times better accuracy than a random labelling strategy within approximately one second.
识别和分类雷达信号及其辐射源的过程目前是电磁环境中作战活动的关键要素。这种类型的系统，称为电子情报分类系统，是被动解决方案，可检测、处理和分析无线电电子信号，并在数据处理的最后阶段提供有关已识别发射源的独特信息。上述类型系统中的数据处理是一个非常复杂的问题，它基于先进的机器学习算法、人工神经网络、分形分析、脉冲内分析、无意带外发射分析以及这些方法的混合。目前，还没有一种最佳方法能够明确识别同一类型雷达发射源的特定副本。本文试图在所有六种情况下，在可比的测量条件下分析由同一类型的六个雷达产生的雷达信号。本文提出了用于电子情报系统的SEI模块的概念。主要目的是进行深入分析，其目的是识别那些雷达的特定副本。本研究的开创性在于应用了作者的数据粒子几何划分算法，目前国际出版物报告中尚无该算法的相关参考。研究表明，将数据粒子几何划分算法应用于同一雷达类型的六个副本的SEI过程，在大约一秒钟内，其准确率几乎比随机标记策略高出近三倍。
