Deinterleaving Radar Pulse Train Using Neural Networks

Erdogan, Alex; George, Kiran

doi:10.1109/cse/euc.2019.00036

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…However, this method, which focuses on removing noise, has a disadvantage, in that it is not possible to extract a jittered PRI pulse train composed of a certain range of noise. Alex Erdogan et al [24] proposed a method of estimating PRI using the CWT result of the input pulse trains as an input to the MLP network. In [25], a method of tracking pulse amplitude to improve the performance of deinterleaving was proposed.…”

Section: A Deinterleavingmentioning

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: A Deinterleavingmentioning

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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“…The cluster and SVM classifier were employed to interleave mixed signals with similar pulse parameters in [167]. In [168], MLP structure was used to deinterleave the radar pulse train. As for denoising aspects, RNNs was used for denoising the pulse train in [164].…”

Section: A Preprocessingmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

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Attention driven CWT-deep learning approach for discrimination of Radar PRI modulation

Sharma,

Sarma

2024

Physical Communication

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Deinterleaving Radar Pulse Train Using Neural Networks

Cited by 3 publications

References 8 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

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Attention driven CWT-deep learning approach for discrimination of Radar PRI modulation

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