A Complete Framework of Radar Pulse Detection and Modulation Classification for Cognitive EW

Yar, Ersin; Kocamis, Mehmet Burak; Orduyilmaz, Adnan; Serin, Mahmut; Efe, Murat

doi:10.23919/eusipco.2019.8903045

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…IQ 1D time sequences [138], [210], [218], [569]; STFT [133]- [135], [137], [212], [229], [230]; CWTFD [130], [215], [217]- [219], [227]; amplitude-phase shift [211]; CTFD [131], [221], [222]; bivariate image with FST [132]; bispectrum [237]; autocorrelation features [213]- [215]; ambiguity function images [140], [141]; fusion features [139], [220] CNNs [82], [210], [211], [217]- [222], [228]- [231], [233], [237], [569]; RNNs [142]- [144], [216]; DBNs [135], [136], [235], [236]; AEs [222]; SENet [212], [213]; ACSENet…”

Section: Features Models Accuracymentioning

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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Section: Features Models Accuracymentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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“…Nonetheless, in the case of another prominent waveform which is nonlinear frequency modulation (NLFM), the number of devised methods is very restricted. Examples of such AMC algorithms, which distinguish NLFM as a separate class, may be found in [ 16 , 17 ]. However, both papers examine only the single NLFM waveform.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study

Milczarek

Lesnik

Djurović

et al. 2021

Sensors

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Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.

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“…Identification of modulations by the ELINT system in realtime is still a challenge. Various digital methods are discussed for modern digital implementation [16][17][18][19][20][21] and decision-theoretic approaches are mentioned for modulation classification [22][23][24][25][26] .…”

Section: Introductionmentioning

confidence: 99%

FPGA based Identification of Frequency and Phase Modulated Signals by Time Domain Digital Techniques for ELINT Systems

2021

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In this paper, a decision tree algorithm based on time-domain digital technique is developed for the identification and classification of diverse radar intra-pulse modulated signals for the electronic intelligence system in real-time. This includes linear frequency modulation, non-linear frequency modulation, stepped frequency modulation and bi-phase modulation. The received signal is digitised and the instantaneous phase and high accuracy instantaneous frequency are estimated. The instantaneous amplitude is also estimated to get the start and stop of the pulse. Instantaneous parameters are estimated using a moving autocorrelation technique. The proposed algorithm is employed on the instantaneous frequency and the modulation is identified. The modulation type and modulation parameter are important for unique radar identification when similar radars are operating in a dense environment. Simulations are carried out at various SNR conditions and results are presented. The model for algorithm is developed using a system generator and implemented in FPGA. These results are compared when the proposed algorithm is used with the existing digital in-phase and quadrature-phase (DIQ) technique of instantaneous frequency and amplitude estimation.

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A Complete Framework of Radar Pulse Detection and Modulation Classification for Cognitive EW

Cited by 17 publications

References 13 publications

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study

FPGA based Identification of Frequency and Phase Modulated Signals by Time Domain Digital Techniques for ELINT Systems

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