Application of Data Particle Geometrical Divide Algorithms in the Process of Radar Signal Recognition

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 19; p. 8183
• Main Authors: Dudczyk, Janusz, Rybak, Łukasz
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 30.09.2023; MDPI
• Subjects: Algorithms; Artificial intelligence; Classification; Data mining; data particle; Decision making; Electromagnetism; geometrical divide; imbalanced data sets; Machine learning; Military aspects; Neural networks; Propagation; Radar; radar identification; radar recognition; Radar systems; Radiation; Specific Emitter Identification; Unmanned aerial vehicles
• ISSN: 1424-8220; 1424-8220
• 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.