Browsing by Author "Gençol, Kenan"

Now showing 1 - 8 of 8

Design of a Scenario-Based Synthetic Mixed Pulse Generator;
(2013) Gençol,K.; Kara,A.; At,N.; Department of Electrical & Electronics Engineering
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. © 2013 IEEE.
Improvements on Deinterleaving of Radar Pulses in Dynamically Varying Signal Environments
(Academic Press inc Elsevier Science, 2017) Gencol, Kenan; Kara, Ali; At, Nuray; Department of Electrical & Electronics Engineering
An electronic support system receiver which is a passive receiver picks up an interleaved stream of pulses and extracts their pulse parameters. These parameters are sent to a deinterleaving subsystem which sorts them and forms pulse cells that each are assumed to belong to a specific emitter. In this paper, we develop a method for this task of deinterleaving of radar pulse sequences. For this aim, a novel pulse amplitude tracking algorithm is proposed for dynamically varying signal environments wherein radar parameters can change abruptly. This method particularly works for air-to-air engagements where pulse amplitude distortion due to channel effects can be considered negligible. Simulation results show that the proposed algorithm incorporated with a clustering algorithm improves deinterleaving of radar emitters that have agile pulse parameters such as airborne radars. (C) 2017 Elsevier Inc. All rights reserved.
New Wavelet-Based Features for the Recognition of Jittered and Stagger Pri Modulation Types
(Ieee, 2015) Gencol, Kenan; Kara, Ali; At, Nuray; 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.
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.
Performance Evaluation of Self Organizing Neural Networks for Clustering in Esm Systems
(Ieee, 2014) Gencol, Kenan; Tora, Hakan; 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.
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.
A Wavelet-Based Feature Set for Recognizing Pulse Repetition Interval Modulation Patterns
(2016) Gençol, Kenan; At, Nuray; Kara, Alı; Department of Electrical & Electronics Engineering
This paper presents a new feature set for the problem of recognizing pulse repetition interval (PRI) modulation patterns. The recognition is based upon the features extracted from the multiresolution decomposition of different types of PRI modulated sequences. Special emphasis is placed on the recognition of jittered and stagger type PRI sequences due to the fact that these types of PRI sequences appear predominantly in modern electronic warfare environments for some specific mission requirements and recognition of them is heavily based on histogram features. We test our method with a broad range of PRI modulation parameters. Simulation results show that the proposed feature set is highly robust and separates jittered, stagger, and other modulation patterns very well. Especially for the stagger type of PRI sequences, wavelet-based features outperform conventional histogram-based features. Advantages of the proposed feature set along with its robustness criteria are analyzed in detail.
A Wavelet-Based Feature Set for Recognizing Pulse Repetition Interval Modulation Patterns
(Tubitak Scientific & Technological Research Council Turkey, 2016) Gencol, Kenan; At, Nuray; Kara, Ali; Department of Electrical & Electronics Engineering
This paper presents a new feature set for the problem of recognizing pulse repetition interval (PRI) modulation patterns. The recognition is based upon the features extracted from the multiresolution decomposition of different types of PRI modulated sequences. Special emphasis is placed on the recognition of jittered and stagger type PRI sequences due to the fact that these types of PRI sequences appear predominantly in modern electronic warfare environments for some specific mission requirements and recognition of them is heavily based on histogram features. We test our method with a broad range of PRI modulation parameters. Simulation results show that the proposed feature set is highly robust and separates jittered, stagger, and other modulation patterns very well. Especially for the stagger type of PRI sequences, wavelet-based features outperform conventional histogram-based features. Advantages of the proposed feature set along with its robustness criteria are analyzed in detail.