• Title/Summary/Keyword: Radar Network

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A Study on the Netted Radar Information Network (Jamming 효과를 고려한 Netted 레이다의 정보통합망 설계에 관한 연구)

  • 김춘길;이형재
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.17 no.4
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    • pp.398-414
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    • 1992
  • For designing the radar integrated data network, we construct the network structure with a spatial hierarchy decomposition scheme. The RIDN can be decomposed into several subnet classes, those of which are composed of the several group classes of radar sits, In a group class, the communication nodes of a radar site are modeled by the software modules formulated with the statistical attributes of discrete events. And we get the analysis over the network through the separately constructed infra group level models which were coded with the C language. After constructing the local area network with these infra models through the proper data links. We got the analysis of the communication performance of inner models and the global network.

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Automatic Intrapulse Modulated LPI Radar Waveform Identification (펄스 내 변조 저피탐 레이더 신호 자동 식별)

  • Kim, Minjun;Kong, Seung-Hyun
    • 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.

Analysis of Vessel Traffic in Tokyo Bay Observed by New Remote Radar Network System

  • Okano, Tadashi;Ohtsu, Kohei;Hagiwara, Hideki;Shoji, Ruri;Tamaru, Hitoi;Liu, Shun
    • Proceedings of the Korean Institute of Navigation and Port Research Conference
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    • 2004.08a
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    • pp.208-216
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    • 2004
  • Since 2000, the authors have been developing remote radar network system to observe the vessel traffic in Tokyo Bay. In December 2002, the first operational remote radar station was set at the National Defense Academy in Yokosuka, and vessel traffic observation was started. However, it was impossible to perform accurate observation in the northern part of Tokyo Bay by this Yokosuka radar station only. In September 2003, the second remote radar station and AIS receiving station were installed at Higashi Ogishima in Kawasaki. This second radar enabled us to carry out accurate observation in that area. Both radars can be remotely controlled from the monitoring station in Tokyo University of Marine Science and Technology. On September 30 and October 1,2003, the vessel traffic observation was carried out using both radars. Combining radar images observed by both radars, the ships' tracks were taken and the dangerous ships were extracted by using SJ value and Bumper Model. The time changes of dangerous ship density in some areas in Tokyo Bay and utilization ratio of the traffic routes were also investigated. In addition, analyzing the AIS date received at Kawasaki station, the positions and speed vectors of the ships equipped with AIS were shown.

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Research for Radar Signal Classification Model Using Deep Learning Technique (딥 러닝 기법을 이용한 레이더 신호 분류 모델 연구)

  • Kim, Yongjun;Yu, Kihun;Han, Jinwoo
    • 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.

Chaff Echo Detecting and Removing Method using Naive Bayesian Network (나이브 베이지안 네트워크를 이용한 채프에코 탐지 및 제거 방법)

  • Lee, Hansoo;Yu, Jungwon;Park, Jichul;Kim, Sungshin
    • Journal of Institute of Control, Robotics and Systems
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    • v.19 no.10
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    • pp.901-906
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    • 2013
  • Chaff is a kind of matter spreading atmosphere with the purpose of preventing aircraft from detecting by radar. The chaff is commonly composed of small aluminum pieces, metallized glass fiber, or other lightweight strips which consists of reflecting materials. The chaff usually appears on the radar images as narrow bands shape of highly reflective echoes. And the chaff echo has similar characteristics to precipitation echo, and it interrupts weather forecasting process and makes forecasting accuracy low. In this paper, the chaff echo recognizing and removing method is suggested using Bayesian network. After converting coordinates from spherical to Cartesian in UF (Universal Format) radar data file, the characteristics of echoes are extracted by spatial and temporal clustering. And using the data, as a result of spatial and temporal clustering, a classification process for analyzing is performed. Finally, the inference system using Bayesian network is applied. As a result of experiments with actual radar data in real chaff echo appearing case, it is confirmed that Bayesian network can distinguish between chaff echo and non-chaff echo.

Using Hierarchical Performance Modeling to Determine Bottleneck in Pattern Recognition in a Radar System

  • Alsheikhy, Ahmed;Almutiry, Muhannad
    • International Journal of Computer Science & Network Security
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    • v.22 no.3
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    • pp.292-302
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    • 2022
  • The radar tomographic imaging is based on the Radar Cross-Section "RCS" of the materials of a shape under examination and investigation. The RCS varies as the conductivity and permittivity of a target, where the target has a different material profile than other background objects in a scene. In this research paper, we use Hierarchical Performance Modeling "HPM" and a framework developed earlier to determine/spot bottleneck(s) for pattern recognition of materials using a combination of the Single Layer Perceptron (SLP) technique and tomographic images in radar systems. HPM provides mathematical equations which create Objective Functions "OFs" to find an average performance metric such as throughput or response time. Herein, response time is used as the performance metric and during the estimation of it, bottlenecks are found with the help of OFs. The obtained results indicate that processing images consumes around 90% of the execution time.

Target Prioritization for Multi-Function Radar Using Artificial Neural Network Based on Steepest Descent Method (최급 강하법 기반 인공 신경망을 이용한 다기능 레이다 표적 우선순위 할당에 대한 연구)

  • Jeong, Nam-Hoon;Lee, Seong-Hyeon;Kang, Min-Seok;Gu, Chang-Woo;Kim, Cheol-Ho;Kim, Kyung-Tae
    • The Journal of Korean Institute of Electromagnetic Engineering and Science
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    • v.29 no.1
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    • pp.68-76
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    • 2018
  • Target prioritization is necessary for a multifunction radar(MFR) to track an important target and manage the resources of the radar platform efficiently. In this paper, we consider an artificial neural network(ANN) model that calculates the priority of the target. Furthermore, we propose a neural network learning algorithm based on the steepest descent method, which is more suitable for target prioritization by combining the conventional gradient descent method. Several simulation results show that the proposed scheme is much more superior to the traditional neural network model from analyzing the training data accuracy and the output priority relevance of the test scenarios.

Radar Rainfall Adjustment by Artificial Neural Network and Runoff Analysis (신경망에 의한 레이더강우 보정 및 유출해석)

  • Kim, Soo Jun;Kwon, Young Soo;Lee, Keon Haeng;Kim, Hung Soo
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.30 no.2B
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    • pp.159-167
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    • 2010
  • The purpose of this study is to get the adjusted radar rainfalls by ANN(Artificial Neural Network) method. In the case of radar rainfall, it has an advantage of spatial distribution characteristics of rainfall while point rainfall has an advantage at the point. Therefore we adjusted the radar rainfall by ANN method considering the advantages of two rainfalls of radar and point. This study constructed two ANN models of Model I and Model II for radar rainfall adjustment. We collected the three rainfall events and adjusted the radar rainfall for Anseong-cheon basin. The two events were inputted into the Modeland Model to derive the optimum parameters and the rest event was used for validation. The adjusted radar rainfalls by ANN method and the raw radar rainfall were used as the input data of ModClark model which is a semi-distributed model to simulate the runoff. As the results of the simulation, the runoff by raw radar rainfall were overestimated but the peak time and peak runoff from the adjusted rainfall by ANN were well fitted to the observed hydrograph.

A study on Conditions of Frequency Coordination for High Speed Radio Access Network in domestic 5㎓ Band (국내 5㎓ 대역 초고속 무선 접속망의 공유조건 연구)

  • 박진아;박승근;박덕규;오용선
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • 2000.10a
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    • pp.247-252
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    • 2000
  • In this paper, we discuss frequency allocation and sharing for high speed radio access network in domestic 5㎓ the band. In order to evaluate the possibility of frequency sharing between meterological radar and high speed radio access network we analyses radio interference of meterological radar by means of minimum coupling loss method and Monte Carlo simulation And simulations show that it is necessary to use DFS(Dynamic Frequency Selection) scheme for frequency sharing between meterological radar and high speed radio access network.

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A study on Conditions of Frequency Coordination for High Speed Radio Access Network in domestic 5GHz Band (국내 5GHz대역 초고속 무선 접속망의 공유조건 연구)

  • 박진아;박승근;박덕규;오용선
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.4 no.4
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    • pp.751-758
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    • 2000
  • In this paper, we discuss frequency allocation and sharing for high speed radio access network in domestic 5GHz the band. In order to evaluate the possibility of frequency sharing between meteorological radar and high speed radio access network, we analyses radio interference of meteorological radar by means of minimum coupling loss method and Monte Carlo simulation. And simulations show that it is necessary to use DFS(Dynamic Frequency Selection) scheme for frequency sharing between meteorological radar and high speed radio access network.

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