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Author: Gencol, KenanPublisher: Ieee

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Now showing 1 - 3 of 3
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    Citation - WoS: 1
    Design of a Scenario-Based Synthetic Mixed Pulse Generator
    (Ieee, 2013) Gencol, Kenan; Kara, Ali; At, Nuray
    Electronic Support Measures (ESM) plays an important role in modern Electronic Warfare (EW) systems. The main purpose of an ESM system is to intercept as many emissions as it can, and then to deinterleave mixed streams of pulses that are interleaved in natural time of arrival order, and thus to identify surrounding emissions. In real life, such systems may encounter with a continuous stream of pulses accompanied by many imperfections, and it should work on real-time basis. In order to handle such circumstances and to develop better deinterleaving algorithms, a simulation tool is needed. In this study, design of a scenario-based mixed pulse generator (simulator) is presented.
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    Citation - WoS: 2
    New Wavelet-Based Features for the Recognition of Jittered and Stagger Pri Modulation Types
    (Ieee, 2015) Gencol, Kenan; Kara, Ali; At, Nuray
    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.
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    Performance Evaluation of Self Organizing Neural Networks for Clustering in Esm Systems
    (Ieee, 2014) Gencol, Kenan; Tora, Hakan
    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.