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Article A Wavelet-Based Feature Set for Recognizing Pulse Repetition Interval Modulation Patterns(2016) Gençol, Kenan; At, Nuray; Kara, AlıThis 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.Article Citation - WoS: 27Citation - Scopus: 35A Wavelet-Based Feature Set for Recognizing Pulse Repetition Interval Modulation Patterns(Tubitak Scientific & Technological Research Council Turkey, 2016) Gencol, Kenan; At, Nuray; Kara, AliThis 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.Article Citation - WoS: 36Citation - Scopus: 38Improvements on Deinterleaving of Radar Pulses in Dynamically Varying Signal Environments(Academic Press inc Elsevier Science, 2017) Gencol, Kenan; Kara, Ali; At, NurayAn 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.
