• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.1
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• pp.233-237
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• 2014

According to advance of radar technology and increase of air threat, 3D surveillance radars are preferred as shipboard equipments to shorten reaction time. In this paper, reaction time against air target was calculated by simulation in each case, 2D and 3D surveillance radar and it was suggested that a few parameters including purpose of warships, performance of shipboard equipments, threat of surrounding countries and budget to select the reasonable type of radar.

• Journal of the Korea Society for Simulation
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• v.26 no.2
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• pp.51-58
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• 2017

This research focuses on convenient radar received power prediction method for detection predictions of North Korea SLBM(Submarine Launched Ballistic Missile). Recently, North Korea tested launching of SLBM which is threatening international security. Therefore, for active respondence to these threat, it is essential to analyze the radar detection prediction of SLBM. In this point of view, this work suggests a method for detection predictions for SLBM by simulating of RCS(Radar Cross Section) and wave propagation.

• 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 Journal of Korean Institute of Next Generation Computing
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• v.13 no.4
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• pp.112-123
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• 2017

Research for the inverse estimation of RF threats and the efficient electronic attack based on the parameters of the electronic information has been active in the electronic warfare (EW) situations. In this paper, an EW transmission simulator is constructed from the modeling of radar threats, EW receivers, and propagation environments with the collected electronic information in order to verify the performance of the inverse estimation algorithm in various and practical EW situations. In simulation results, we show that the range tracking error and angle tracking error are produced within ten meters and one degree, respectively. And also, we show that the changing relations between the angle tracking error and the parameters of the monopulse angle tracking radar such as the beamwidth and squint angle in simulation results correspond with those in the theoretical modeling. Accordingly, the constructed EW simulator can be used to observe the modifying characteristics of the electronic information in transmission environments, and then, to evaluate the performance of the inverse estimation system in various EW situations.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.669-676
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• 2016

North Korea has various ballistic missiles from short range to long range such as inter continental ballistic missiles. Short range ballistic missiles such as SCUD series are threatening to Korea peninsula. Therefore Korea is constructing various missile defense systems to protect country. Parameters influencing the received power from the target to the radar are transmitting power, antenna gain, carrier frequency, RCS(Radar Cross Section) of target and distance from radar to target. Especially, RCS and distance from target are not radar performance defined parameters but external parameters. Therefore radar deployment position that large RCS can be observed and target to radar distance should be considered in parallel to improve target detection probability. In this paper, RCS pattern of SCUD-B ballistic missile is calculated, received power is analyzed based on radar deployment position during ballistic missile trajectory and methode for optimum radar deployment position to improve target detection probability is suggested.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.34 no.1
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• pp.74-80
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• 2006

This paper addresses the trajectory optimization of Unmanned Aerial Vehicles(UAVs) under multiple ground threats like enemy's anti-air radar sites. The power of radar signal reflected by the vehicle and the flight time are considered in the performance cost to be minimized. The bank angle is regarded as control input for a 1st-order lag vehicle, and input parameter optimization method based on Sequential Quadratic Programming (SQP) is used for trajectory optimization. The proposed path planning method provides more practical trajectories with enhanced survivability than those of Voronoi diagram method.

• Convergence Security Journal
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• v.20 no.1
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• pp.77-84
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• 2020

Due to the lack of a system that can detect/identify and strike North Korea's small unmanned aerial vehicles, no immediate response was made. In order to solve the problem of response, we should prepare for threats by developing weapons systems that can be immediately hit when identifying small-sized unmanned aerial vehicle detection radar and creating specialized research organizations for research and development of equipment.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.9
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• pp.947-954
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• 2010

The decision of threat target in the MWR(Missile Warning Radar) of GVES(Ground Vehicle Equipment System) such as MBT(Main Battle Tank) is very important. Threat decision is judged by angular rate and the accurate azimuth calculation for good threat decision is very important. The angular rate is dependent upon the direction of an approaching target. The target is classified into a threat or non-threat using a boundary condition of the angular rate. This paper presents the eighth azimuth calculation methods and compares the results.

• Journal of the Korea Society for Simulation
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• v.28 no.1
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• pp.67-79
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• 2019

A warship equipped with Multi Function Radar(MFR) performs operations by evaluating the degree of threats based on threats' symptom and allocating the resource of MFR to the corresponding threats. This study suggests a simulation-based approach and greedy algorithm in order to effectively allocate the resource of an MFR for warships, and compares these two approaches. As a detection probability function depending on the amount of allocations to each threat, we consider linear and exponential functions. Experimental results show that both the simulation-based approach and greedy algorithm allocate resource similarly to the randomly generated threats, and the greedy algorithm outperforms the simulation-based approach in terms of computational perspective. For a various cases of threats, we analyze the results of MFR resource allocation using the greedy algorithm.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.12
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• pp.1333-1339
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• 2009

A MWR(Missile Warning Radar) of GVES(Ground Vehicle Equipment System) has to effectively decide the threat for a detected target. Linear Approximation Fitting(LAF) and Weighted Linear Approximation Fitting(WLAF) algorithm is proposed as algorithm for a threat decision method. The target is classified into a threat or non-threat using a boundary condition of the angular rate, and the boundary condition is determined using probability model simulation. This paper confirms the performance of proposed threat decision algorithm using measurement.