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Journal of Sensors
Volume 2016, Article ID 1879327, 9 pages
http://dx.doi.org/10.1155/2016/1879327
Research Article

Radar Emission Sources Identification Based on Hierarchical Agglomerative Clustering for Large Data Sets

R&D Department, WB Electronics S.A., Poznanska 129/133 Street, 05-850 Ozarow Mazowiecki, Poland

Received 11 January 2016; Revised 22 March 2016; Accepted 27 April 2016

Academic Editor: Fanli Meng

Copyright © 2016 Janusz Dudczyk. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

More advanced recognition methods, which may recognize particular copies of radars of the same type, are called identification. The identification process of radar devices is a more specialized task which requires methods based on the analysis of distinctive features. These features are distinguished from the signals coming from the identified devices. Such a process is called Specific Emitter Identification (SEI). The identification of radar emission sources with the use of classic techniques based on the statistical analysis of basic measurable parameters of a signal such as Radio Frequency, Amplitude, Pulse Width, or Pulse Repetition Interval is not sufficient for SEI problems. This paper presents the method of hierarchical data clustering which is used in the process of radar identification. The Hierarchical Agglomerative Clustering Algorithm (HACA) based on Generalized Agglomerative Scheme (GAS) implemented and used in the research method is parameterized; therefore, it is possible to compare the results. The results of clustering are presented in dendrograms in this paper. The received results of grouping and identification based on HACA are compared with other SEI methods in order to assess the degree of their usefulness and effectiveness for systems of ESM/ELINT class.