Browsing by Author "Gencol,K."

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    New Wavelet-Based Features for the Recognition of Jittered and Stagger Pri Modulation Types;
    (Institute of Electrical and Electronics Engineers Inc., 2015) Gencol,K.; Kara,A.; At,N.; Department of Electrical & Electronics Engineering
    In dense electronic warfare environments, numerous emitters can be active simultaneously and an interleaved stream of pulses in natural time of arrival order is received by the Electronic Support Measures (ESM) receiver. It is the task of the ESM system to de-interleave this mixed pulse sequence and thus to identify the surrounding threatening emitters. In this processing, pulse repetition interval (PRI) modulation recognition has a significant role due to the fact that it can reveal the hidden patterns inside pulse repetition intervals and thus help identify the emission source and its functional purpose. In this paper, we propose new wavelet-based features for the recognition of jittered and stagger PRI modulation types. The recognition of these types are heavily based on histogram features. Experimental results show that the proposed feature set have very high recognition rates and outperform histogram based methods. © 2015 IEEE.
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    Performance Evaluation of Self Organizing Neural Networks for Clustering in Esm Systems;
    (IEEE Computer Society, 2014) Gencol,K.; Tora,H.; Airframe and Powerplant Maintenance; Department of Electrical & Electronics Engineering
    Electronic Support Measures (ESM) system is an important function of electronic warfare which provides the real time projection of radar activities. Such systems may encounter with very high density pulse sequences and it is the main task of an ESM system to deinterleave these mixed pulse trains with high accuracy and minimum computation time. These systems heavily depend on time of arrival analysis and need efficient clustering algorithms to assist deinterleaving process in modern evolving environments. On the other hand, self organizing neural networks stand very promising for this type of radar pulse clustering. In this study, performances of self organizing neural networks that meet such clustering criteria are evaluated in detail and the results are presented. © 2014 IEEE.