• ETRI Journal
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• v.43 no.6
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• pp.991-1003
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• 2021

Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM-CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at -2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.3 s.309
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• pp.61-67
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• 2006

In electronic support systems, the analysis of PRI modulation characteristics for radar signals has attracted much interest because of the problem of the identification ambiguity in dense electronic warfare signal environments. A new method of recognizing the PRI modulation types of radar pulse signals is proposed for electronic support. The proposed method recognizes the PRI modulation types using the classifiers which are based on the property of the linear autocorrelation of the PRI sequences for each PRI modulation type. In addition, the proposed method estimates the PRI modulation period for the PRI modulation types with the periodicity. Simulation results are presented to show the performance of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.6
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• pp.372-377
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• 2003

Many of modem radars use modulated pulse repetition intervals for the purpose of anti-aliasing and ECCM. The interception, analysis and identification of radar signals is a major function of a radar intercept receiver. In this paper, we discuss the identification of pulse repetition intervals modulation of radar signals which is one of the major parameters for the analysis of radar. We proposed a new algorithm based on Markov models approach. This approach is shown to be reliable and robust to the missing pulses, as well as to require only relatively few pulse data.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.2
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• pp.425-430
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• 2017

This paper presents a novel method for identification of FSK modulated radar signal. Three features which measure the number of frequency tones, the regularity of the frequency shifting, and the diversity of power spectrum of detected radar signal, are introduced. A Two-step combined maximum likelihood classifier was used to identify the details of the detected FSK signal; the modulation order and the use of Costas code. We attempted to divide FSK signal into binary FSK, ternary FSK, 8-ary FSK, and FSK with Costas code of length 7. The simulation results indicated that the proposed methods achieves an averaged identification accuracy was 99.93% at a signal-to-noise of 0 dB.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.4
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• pp.36-45
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• 2008

LYNX ESM system operates digital receiver for analyzing radar of intrapulse modulation signal. This paper contains DC offset compensation method of IQ channels, imbalancing comensation method of amplitude and phase, precisional PW measurement using adaptive threshold set, analyzing algorithm of intrapulse modulation signals. Its effectiveness was proven by technical and operational test.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.133-140
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• 2018

In electronic warfare(EW), low probability of intercept(LPI) radar signal is a survival technique. Accordingly, identification techniques of the LPI radar waveform have became significant recently. In this paper, classification and extracting parameters techniques for 7 intrapulse modulated radar signals are introduced. We propose a technique of classifying intrapulse modulated radar signals using Convolutional Neural Network(CNN). The time-frequency image(TFI) obtained from Choi-William Distribution(CWD) is used as the input of CNN without extracting the extra feature of each intrapulse modulated radar signals. In addition a method to extract the intrapulse radar modulation parameters using binary image processing is introduced. We demonstrate the performance of the proposed intrapulse radar waveform identification system. Simulation results show that the classification system achieves a overall correct classification success rate of 90 % or better at SNR = -6 dB and the parameter extraction system has an overall error of less than 10 % at SNR of less than -4 dB.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.700-702
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• 2016

The Doppler Radar that mounted on Electro Optical Tracking System has been operated to measure range and velocity during the initial mission of space launch vehicle at Naro space center. In this paper, we mentioned configuration of MFCW(Multi frequency Continuous Wave) and FMCW(Frequency Modulation Continuous Wave) Doppler Radar on Electro Optical Tracking System and described method of range measurement.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.2
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• pp.170-178
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• 2019

Classification of radar signals in the field of electronic warfare is a problem of discriminating threat types by analyzing enemy threat radar signals such as aircraft, radar, and missile received through electronic warfare equipment. Recent radar systems have adopted a variety of modulation schemes that are different from those used in conventional systems, and are often difficult to analyze using existing algorithms. Also, it is necessary to design a robust algorithm for the signal received in the real environment due to the environmental influence and the measurement error due to the characteristics of the hardware. In this paper, we propose a radar signal classification method which are not affected by radar signal modulation methods and noise generation by using deep learning techniques.

• Proceedings of the KSRS Conference
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• v.1
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• pp.39-42
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• 2006

The paper reports on the first experimental evidence for space-observed manifestation of the open ocean tsunami in the microwave radar backscatter (in C- and Ku-bands). Significant variations of the radar cross section synchronous with the sea level anomaly were found in the geophysical data record of the altimetry satellite Jason-1 for the track which crossed the head wave of the catastrophic tsunami of 26 December 2004. The simultaneous analysis of the available complementary data provided by the satellite three-channel radiometer enabled us to exclude meteorological factors as possible causes of the observed signal modulation. A possible physical mechanism of modulation of short wind waves due to transformation of the thin boundary layer in the air by a tsunami wave is discussed. The results open new possibilities of monitoring tsunamis from space..

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.1
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• pp.23-29
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• 2022

As the application field of radar is expanded and the bandwidth increases, the number of radar sensors operating at the same frequency is continuously increasing. In this paper, we propose a method of analyzing interference when two pulse doppler radars are operated at the same frequency with different waveform which are designed independently. In addition, we show that even for a previously designed LFM waveforms, the interference can be suppressed without affecting the performance by changing the sign of the frequency slope by increasing/decreasing, or by modulating the pulses by the different codes. The interference suppression by different slopes is more effective for similar waveform and the suppression by the codes increases as the number of pulses increases. We expect this result can be extended to the cases where multiple radars are operated at the same frequency.