Pulse Repetition Interval Modulation Recognition Using Symbolization

Song, Kyu-Ha; Lee, Dong-Weon; Han, Jin-Woo; Park, Byung-Koo

doi:10.1109/dicta.2010.96

Cited by 17 publications

(6 citation statements)

References 7 publications

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“…Another method [30] of utilizing features such as stationary and symmetry was also proposed. The authors in [31] used features such as modified Shannon's entropy and kurtosis after symbolizing TOA sequences, and in [32], features such as normalized jump energy and SP-curve for PRI modulation recognition were used. In addition, a study on various emitter scenarios was also presented that used a wavelet transform and histogram in combination through various procedures [33].…”

Section: B Pri Modulation Recognitionmentioning

confidence: 99%

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

Han

Park

2021

IEEE Access

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In the modern electronic warfare signal environment, multiple radar signals of high density are mixed and received, and separating them into signals for each emitter is an essential step for emitter identification. Each radar has its own pulse repetition interval (PRI), which is a key parameter for deinterleaving pulse trains. The PRI is modulated in various forms depending on the purpose of the radar operation, and analyzing the mean PRI and the modulation type of PRI is the core of electronic warfare signal processing. Many existing papers have tried separate independent approaches for deinterleaving and for PRI modulation recognition. However, many distortions are unintentionally generated in the process of extracting the pulse train using the PRI estimated through deinterleaving for the PRI modulation recognition. This degrades the the modulation recognition performance. In this paper, we propose a unified method for the deinterleaving and PRI modulation recognition of radar pulses using deep learning-based multitasking learning. The simulation results demonstrate the good performance of the proposed method for deinterleaving and modulation recognition, compared to the conventional method, and prove that the proposed method is robust in noisy radar signal environments.

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Section: B Pri Modulation Recognitionmentioning

confidence: 99%

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

Han

Park

2021

IEEE Access

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“…Other processes including PRI modulation recognition are usually performed later. Many researchers try to classify radar pulses into the seven aforementioned categories assuming that the deinterleaving process has been performed in advance [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. However, the deinterleaving process is highly influenced by the types of the existing PRI modulations especially while complex modulations such as stagger and jitter are present.…”

Section: Related Workmentioning

confidence: 99%

“…The other important issue in this framework is the classification method. In a number of studies, such as [8,[10][11][12][13][14][15][16], Decision Tree is used as the classifier. Multiple Layer Perceptron (MLP) is another useful one, which is used in Ref.…”

Section: Related Workmentioning

confidence: 99%

Automatic recognition of pulse repetition interval modulation using temporal convolutional network

Dadgarnia

Sadeghi

2021

IET Signal Processing

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Pulse Repetition Interval (PRI) modulation recognition is a key issue in radar identification process in modern electronic intelligent (ELINT) and electronic support measure (ESM) systems. In this study, a novel approach based on the intrinsic property of the temporal convolutional network (TCN) is presented for PRI modulation type recognition. Since a causal TCN is used for this purpose, the method is suitable for online ESM and ELINT analysis. The simulation results show that the method accurately classifies seven types of PRI modulation including simple, dwell and switch, stagger, jitter, agile, sliding and periodic modulations in realistic scenarios. In order to investigate the performance of the method when the problem of missing or spurious pulses occurs, the impacts of these problems are examined, separately and together, on the recognition process. It is shown that the method works effectively even with a relatively high percentage of missing and/or spurious pulses (up to 30%). The method also works effectively in the presence of unintentional jitter and large outliers, which can be due to radar antenna scan. The experimental results on real data confirm that the proposed method performs accurately and effectively in real world scenarios.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…The PRI pattern of conventional radar signals refers to the value and ordering of multiple adjacent inter-pulse intervals within a cycle, and the other pulse parameters like PW and frequency may also be included. Precise analysis of radar PRI pattern plays an important role in many radar reconnaissance problems such as PRI modulation recognition [5][6][7] and pulse deinterleaving [8][9][10][11][12]. The PRI modulation recognition methods extract features from sequential differential timeof-arrival (DTOA) to distinguish between different types of PRIs, for example, constant, stagger and jitter PRI.…”

Section: Introductionmentioning

confidence: 99%

Automatic pulse repetition pattern reconstruction of conventional radars

Liu

Kang

Chai³

2021

IET Radar Sonar & Navi

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Signals of radars with fixed, stagger and other conventional pulse repetition intervals (PRI) occupy a large proportion in most radar reconnaissance datasets. Automatic and accurate reconstruction of the PRI patterns of these radars is an important task in realizing intelligent processing of radar reconnaissance data. In order to address this problem, this article proposes an automatic method based on the extraction and extension of frequent differential time-of-arrival (DTOA) items, which can be combined with other pulse parameters such as pulse width. The values and ordering of the frequent items are determined to reconstruct a complete PRI cycle, and the radar pulses are deinterleaved at the same time as a by-product. Compared with the commonly used onedimensional radar reconnaissance parameters such as framework PRI or typical PRI set, the reconstructed PRI pattern contains more information about radar operational modes and can be applied to realize online and parallel pulse deinterleaving and more accurate radar type classification.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Pulse Repetition Interval Modulation Recognition Using Symbolization

Cited by 17 publications

References 7 publications

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

A Unified Method for Deinterleaving and PRI Modulation Recognition of Radar Pulses Based on Deep Neural Networks

Automatic recognition of pulse repetition interval modulation using temporal convolutional network

Automatic pulse repetition pattern reconstruction of conventional radars

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