• Journal of IKEEE
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• v.22 no.1
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• pp.162-167
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• 2018

In this paper, we propose a method to classify radar signals according to the jamming technique by applying the machine learning to parameter data extracted from received radar signals. In the present army, the radar signal is classified according to the type of threat based on the library of the radar signal parameters mostly built by the preliminary investigation. However, since radar technology is continuously evolving and diversifying, it can not properly classify signals when applying this method to new threats or threat types that do not exist in existing libraries, thus limiting the choice of appropriate jamming techniques. Therefore, it is necessary to classify the signals so that the optimal jamming technique can be selected using only the parameter data of the radar signal that is different from the method using the existing threat library. In this study, we propose a method based on machine learning to cope with new threat signal form. The method classifies the signal corresponding the new jamming method for the new threat signal by learning the classifier composed of the hidden Markov model and the neural network using the existing library data.

• The Magazine of the IEIE
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• v.15 no.1
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• pp.48-56
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• 1988

전술 목적으로 세계 각국의 군에서 많이 사용하고 있는 레이더에 대하여, 그동안의 발전 과정을 고찰해 보고, 현재 각국 군에서 운용중인 레이더를 사용 목적별로 분류, 설명했으며, 2000년대를 향한 앞으로의 기술적인 발전 방향을 검토해 보았다. 레이더의 발전 역사를 초창기, 1940년대, 1950년대, 1960년대, 1970년대, 1980년대 별로 분류하여, 각 연대 별로 레이더에 관련된 기술이나 주요 개발 내용을 기술 했으며, 현재 사용중인 레이더를 지상 레이더, 함정 레이더, 항공기 레이더, 비 군사적 사용 분야별로 나누어 검토해 보았다. 그리고 끝으로 레이더의 기술적인 발전 방향을 레이더의 체계, 안테나, 송수신기, 신호처리 분야별로 핵심기술의 발전 추세를 개괄적으로 분석, 기술하였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.296-302
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• 2021

This paper proposes an approach to the classification of respiratory states of humans based on visual information. An ultra-wide-band radar sensor acquired respiration signals, and the respiratory states were classified based on two-dimensional (2D) images instead of one-dimensional (1D) vectors. The 1D vector-based classification of respiratory states has limitations in cases of various types of normal respiration. The deep neural network model was employed for the classification, and the model learned the 2D images of respiration signals. Conventional classification methods use the value of the quantified respiration values or a variation of them based on regression or deep learning techniques. This paper used 2D images of the respiration signals, and the accuracy of the classification showed a 10% improvement compared to the method based on a 1D vector representation of the respiration signals. In the classification experiment, the respiration states were categorized into three classes, normal-1, normal-2, and abnormal respiration.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.775-778
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• 2016

In this paper, we propose a new method to automatically recognize PRI modulation type of radar signal at ES(Electronic Support) in electronic singal environment. The propose method stores pattern of PRI(Pulse Repetition Interval) of radar signal and uses statistic data, which firstly classifies into 2 classes. Then the proposed method recognizes each PRI signal using statistic characteristic of PRI. We apply various 5 kinds of PRI signal such as constant PRI, jitter PRI, D&S(dwell & switch) PRI, stagger PRI, sliding PRI, etc. The result shows the proposed method correctly identifies various PRI signals.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.11a
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• pp.732-735
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• 2012

본 논문은 지상에서 발생하는 전자파 잡음 신호의 유입에 의해 많은 영향을 받을 수 있는 헬기 등 항공기의 레이더경보수신기(Radar Warning Receiver)에서도 레이더 펄스 신호를 탐지할 수 있는 통신전자파잡음 동적제거 기법을 제안하였다. 본 논문은 지상의 노이즈 신호를 분류하는 방법을 제시하였고, 노이즈 신호 레벨을 판단하여 효과적으로 잡음을 제거하는 알고리즘을 제안하였다.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.137-144
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• 2014

The Electronic Support System collects and analyzes the received radar signals in order to cope with the electronic attack in real-time. The radar-pulse clustering system classifies the radar signals that are considered to be emitted by a single source. This paper proposed a radar-pulse clustering algorithm based on four kinds of features: the direction, frequency, pulse width, and the difference of arrival time between two successive pulses. The experiment results show that the proposing algorithm could trace the moving emitter and classify the timely separated signals into different classes.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.41 no.5
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• pp.9-18
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• 2004

This paper presents a vehicle detecting algorithm using microwave system signals. The Proposed algerian decides the breakpoint of signals using the likelihood criteria. The decided signals are segmented and simplified. The proposed searching algorithm uses the Euclid distance from the weighted signal data. We tested the proposed algorithm to compare with the segmentation which is a method using smoothing and edge detection. We confirm that the proposed algorithm is very useful for detecting vehicles by field test.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1063-1068
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar due to its observation principle and disturb weather forecasting process. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo with data mining techniques. This paper conducts researches about implementation of classification method which can separate the anomalous propagation echo in the raw radar data using naive Bayes classifier with various kinds of observation results. Considering that collected data has a class imbalanced problem, this paper includes SMOTE method. It is confirmed that the fine classification results are derived by the suggested classifier with balanced dataset using actual appearance cases of the echo.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.89-90
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• 2016

Anomalous propagation echo is a kind of abnormal radar signal occurred by irregularly refracted radar beam caused by temperature or humidity. The echo frequently appears in ground-based weather radar. In order to improve accuracy of weather forecasting, it is important to analyze radar data precisely. Therefore, there are several ongoing researches about identifying the anomalous propagation echo all over the world. This paper conducts researches about a classification method which can distinguish anomalous propagation echo in the radar data using naive Bayes classifier and unique attributes of the echo such as reflectivity, altitude, and so on. It is confirmed that the fine classification results are derived by verifying the suggested naive Bayes classifier using actual appearance cases of the echo.

• 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.