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.
Highlights
In the EW aspect, the way to increase the detail level of recognition is the Specific Emitter Identification (SEI) method [1,2,3]
The extraction of distinctive features coming from the out-of-band radiation analysis increases the precision of received results in the radar identification process from 50% to 70% [7]
Another way, which is used in SEI methods, is the analysis of inter-Pulse Repetition Interval modulation and intrapulse analysis of a radar signal
Summary
In the EW aspect, the way to increase the detail level of recognition is the SEI method [1,2,3]. The extraction of distinctive features coming from the out-of-band radiation analysis increases the precision of received results in the radar identification process from 50% to 70% [7] Another way, which is used in SEI methods, is the analysis of inter-Pulse Repetition Interval modulation and intrapulse analysis of a radar signal. As a part of the advanced method of SEI analysis presented in this paper, it should be emphasized that there is a significant fact; namely, all measurement data in the form of recorded radar signals come from a dozen or so working radiolocation devices of the same type It is good to point out the fact that their target is to be implemented to ESM/ELINT systems and to be used in EW in an optimal way, which causes no computational overload to such a recognition system
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