• Journal of Korea Society of Industrial Information Systems
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• v.25 no.4
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• pp.29-38
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• 2020

In this research, we proposed the active pulse classification algorithm using multi-label convolutional neural networks for active sonar system. The proposed algorithm has the advantage of being able to acquire the information of the active pulse at a time, unlike the existing single label-based algorithm, which has several neural network structures, and also has an advantage of simplifying the learning process. In order to verify the proposed algorithm, the neural network was trained using sea experimental data. As a result of the analysis, it was confirmed that the proposed algorithm converged, and through the analysis of the confusion matrix, it was confirmed that it has excellent active pulse classification performance.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.754-756
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• 2013

The modern Electronic Warfare Receivers are required to the current radar technologies like the Low Probability of Intercept(LPI) radars to avoid detection. LPI radars have features of intra-pulse modulation differ from existing radar signals. This features require counterworks such as signal confirmation and identification. Hence this paper presents a study on intra-pulse modulation recognition. The proposed method automatically recognizes intra-pulse modulation types such as LFM and NLFM using classifiers extracted from the features of each intra-pulse modulation. Several simulations are also conducted and the simulation results indicate the performance of the given method.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.7
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• pp.1536-1541
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• 2009

In this paper, we present a novel pulse train identification method for two step stagger pulse train. Generally radar uses a fixed pulse train, and it is easy for electronic warfare system to measure the pulse repeat interval(PRI) and identify the radar. But it is very difficult to measure the PRI of stagger pulse radar because the pulse interval is periodically changed. We suggest a novel method to measure the PRI and identify the radars using the second deviation of pulse train. This method is faster comparing with Histogram method. We have a good PRI measurement results for 2 step stagger signals.

• Journal of the Korea Computer Industry Society
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• v.2 no.10
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• pp.1245-1252
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• 2001

In this paper, we have identified speakers who give digits in Korean. In order to identify speakers, we have utilized the specifie feature parameters which extracted from sound wave. We have noticed that multipulses are present in pitch periods of sound wave, which containes the personal information and depends on the speakers. In this experiment, we have extracted multipulses, and have attempted to identify the speaker by investigating the specific feature parameters of each speaker based on the extracted multipulses.

• Journal of Convergence for Information Technology
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• v.11 no.11
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• pp.17-22
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• 2021

Radar signal analysis of electronic warfare is a technique for identifying a radar type by signal parameters(direction, radion frequency, pulse repetition interval, pulse width, scan period..) extracted from a received radar pulse. However as the modern radar and new threat environments is advanced, radar identification ambiguity arises in the process of identifying the types of radars. In this paper, we analyze the problems of the existing method and propose a new method. This technique determines the validity of the scan period by the difference in the arrival time of the radar pulse and the minimum number of scan period discrimination. Experiments proved that the scan cycle results are derived regardless of the RMS((Root Mean Square) of the input amplitude.

• Journal of Convergence for Information Technology
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• v.8 no.5
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• pp.137-143
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• 2018

Generally, a radar signal is modulated and transmitted in order to avoid signal detection. In electronic warfare, the specification of a radar is recognized by analysing the received radar pulses. In this paper, we propose an algorithm to recognize the PRI (Pulse Repetition Interval) type of radar signals. This algorithm uses the autocorrelation technique applying different comparison ranges according to the PRI type. It applies a short comparison window to stable and staggered PRI, and a relatively large comparison range to jittered PRI. The experiment shows that the proposed algorithm can discriminate the PRI type of radar pulses correctly. For the more, it can find out the stagger level of staggered type of radar signals.

• The Journal of the Acoustical Society of Korea
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• v.38 no.1
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• pp.106-113
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• 2019

In this paper, we propose an algorithm to classify the received active pulse when the active sonar system is operated as a non-cooperative mode. The proposed algorithm uses CNN (Convolutional Neural Networks) which shows good performance in various fields. As an input of CNN, time frequency analysis data which performs STFT (Short Time Fourier Transform) of the received signal is used. The CNN used in this paper consists of two convolution and pulling layers. We designed a database based neural network and a pulse feature based neural network according to the output layer design. To verify the performance of the algorithm, the data of 3110 CW (Continuous Wave) pulses and LFM (Linear Frequency Modulated) pulses received from the actual ocean were processed to construct training data and test data. As a result of simulation, the database based neural network showed 99.9 % accuracy and the feature based neural network showed about 96 % accuracy when allowing 2 pixel error.

• Journal of IKEEE
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• v.22 no.3
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• pp.624-629
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• 2018

Classification of moving targets in Pulse Doppler Radar(PDR) for surveillance and reconnaissance purposes is generally carried out based on listening and training experience of Doppler audio signals by radar operator. In this paper, we proposed the automatic classification method to identify the class of moving target with Doppler audio signals using the Mel Frequency Cepstral Coefficients(MFCC) and the Hidden Markov Model(HMM) algorithm which are widely used in speech recognition and the classification performance was analyzed and verified by simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.611-619
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• 2016

Success in modern war depends on electronic warfare. Therefore, It is very important to identify the kind of Radar PRI modulations in a lot of Radar electromagnetic waves. In this paper, I propose an algorithm to identify Linear up Sliding PRI, Non-Linear up Sliding PRI and Linear Down Sliding PRI, Non-Linear Down Sliding PRI among many Radar pulses. We applied not only the TDOA(Time Difference Of Arrival) concept of Radar pulse signals incoming to antennas but also a rising and falling curve characteristics of those PRI's. After making a program by such algorithm, we input each 40 data to those PRI's identification programs and as a result, those programs fully processed the data in according to expectations. In the future, those programs can be applied to the ESM, ELINT system.

• Proceedings of the KAIS Fall Conference
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• 2012.05b
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• pp.804-807
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• 2012

본 논문에서는 펄스 레이저(LASER) 송신기에서 방사되는 펄스신호를 레이저 감시 시스템에서 실시간으로 수신하여 수신된 레이저 펄스들의 변화 패턴, 특히 Dual & Switch 신호의 패턴을 분석하는 방법에 대하여 기술하였다. Dual & Switch 신호는 펄스반복시간이 주기적으로 변경되어 펄스 패턴을 예측하기가 매우 어렵다. 본 논문에서는 펄스반복간격(PRI)의 차분을 이용하여 고정, 지터, Dual & Switch 신호의 패턴을 확인하는 방법을 제안하였다. 제안된 방법은 Dual & Switch 신호에 대한 신호 식별능력이 가능하여 레이저 감시시스템 등에 사용할 수 있을 것으로 판단된다.