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.

The Ground Moving Target Indication(GMTI) technique can detect the moving targets on land using its Doppler returns. Also, the GMTI system can work in night regardless of the weather condition because it is an active sensor that uses the electromagnetic waves as its source. In order to develop the GMTI system, Constant False Alarm Rate(CFAR) threshold optimization is important because the main performances like detection probability, false alarm rate and Minimum Detectable Velocity(MDV) are related deeply with CFAR threshold. These key variables are used to calculate CFAR threshold and then trade-off between the variables is performed. In this paper, CFAR threshold optimization procedures are introduced, and the optimization results are demonstrated.

There are increasing demands to provide early warning against intruding drones and cope with potential threats. Commercial anti-drone systems are mostly based on simple target detection by radar reflections. In real scenario, however, it becomes essential to obtain drone radar signatures so that hostile targets are recognized in advance. We present experimental test results that micro-Doppler radar signature delivers partial information on multi-rotor platforms and exhibits limited performance in drone recognition and classification. Afterward, we attempt to generate high resolution profile of flying drone targets. To this purpose, wide bands radar signals are employed to carry out inverse synthetic aperture radar(ISAR) imaging against moving drones. Following theoretical analysis, experimental field tests are carried out to acquire real target signals. Our preliminary tests demonstrate that high resolution ISAR imaging provides effective measures to detect and classify multiple drone targets in air.

Recently there is a great interest in the bi-static sonar. However, since the transmitter and the receiver operate on different platforms, it may be necessary to operate the system in a non-cooperative mode. In this situation, the detection and localization performance are limited. Therefore, it is necessary to classify the received pulse from the transmitter to overcome the performance limitation. In this paper, we proposed a robust automatic pulse classification method that can be applied to real systems. The proposed method eliminates the effects of noise and multipath propagation through post-processing and improves the pulse classification performance. We also verified the proposed method through the sea experimental data.

To develop and evaluate the real-time SAR(Synthetic Aperture Radar) motion measurement system, true antenna phase center(APC) positions during SAT(Synthetic Aperture Time) are needed. In this paper, CDGPS(Carrier phase Differential Global Positioning System) post processing method is proposed to get the true APC position for spotlight SAR image formation. The CDGPS position is smoothed to remove high frequency noise which exists inherently in the carrier phase measurement. This paper shows smoothed CDGPS data is enough to provide the true APC for high-quality SAR image formation through motion measurement result, phase error estimation and IRF(Impulse Response Function) analysis.

Estimating reliability of a non-repairable system using the degradation data, variance assumption such as homogeneity (constant) or heteroscedasticity (time-variant) could affect accuracy of reliability estimation. This paper showed reliability estimation and comparison results under normal conditions using accelerated degradation data obtained from destructive measurements, according to variance assumption of the data at each measurement time. Degradation data from three accelerated conditions with stress factors of temperature and humidity were used to estimate reliability. The $B_{10}$ lifetime was estimated as 1243.8 years by constant variance assumption, and 18.9 years by time-variant variance. And variance assumption provided different analysis results of important stresses to reliability. Thus, accurate assumption of variance at each measurement time is required when estimating reliability using degradation data of a non-repairable system.

In this study, we develop a prediction method of cavity shape taking gravity effect and angle of attack of cavitator into consideration simultaneously. Logvinovich's theoretical formulas are superimposed to predict the change of cavity centerline due to both gravity effect and angle of attack of cavitator. It is found that as the angle of attack of cavitator increases, the gravity effect is weakened due to decrease in cavity volume, and even in case of the same angle of attack, cavity shape changes in different ways depending on whether the angle of attack of cavitator is positive or negative. We conclude that cavity shapes are largely affected by the angle of attack of cavitator, and the gravity effect and angle of attack of cavitator should be considered at the same time for the prediction of cavity shape.

Recently a supercavitating underwater vehicle moving at high speed over 200 knots has been of interest for its practical advantage of the dramatic drag reduction. Many experimental and numerical studies have been explored, however most of the studies deal with the case of uniform flows. In this paper, we investigated physical behaviors of the artificial supercavity in a periodic gust flow. Experiments were carried out at a cavitation tunnel of the Chungnam National University(CNUCT), which is equipped to remove the gas supplied from outside of the tunnel. We devised an experimental apparatus generating vertical and horizontal gust flows, and investigated the supercavity formations at different periodic mode of the incoming flow.

Among the techniques of reducing the drag to increase the speed of underwater moving bodies, we studied on the drag reduction method by gas injection. Researches on gas injection method have been paid much attention to reduce the drag of vessels or pipe inner walls. In this study, we used a sintered metal mesh that can uniformly distribute fine bubbles by gas injection method, and applied it to a cylindrical underwater moving body. Using the KRISO medium-sized cavitation tunnel, we measured both the bubble size on the surface of the sintered mesh and the bubble distribution in the boundary layer. Then, drag reduction tests were performed on the cylinder type underwater moving models with cylindrical or round type tail shape. Experiments were carried out based on the presence or absence of tail jet injection. In the experiments, we changed the gas injection amount using the sintered mesh gas injector, and changed flow rate accordingly. As a result of the test, we observed increased bubbles around the body and confirmed the drag reduction as air injection flow rate increased.

As the 4th industrial revolution and artificial intelligence technology develop, it is expected that there will be a revolutionary changes in the security robot. However, artificial intelligence system requires enormous hardwares for tremendous computing loads, and there are many challenges that need to be addressed more technologically. This paper introduces precise tracking control technique of autonomous system that need to move repetitive paths for security purpose. The input feedforward signal is generated by using the inverse based iterative learning control theory for the 2 input 2 output nonminimum-phase system which was difficult to overcome by the conventional feedback control system. The simulation results of the input signal generation and precision tracking of given path corresponding to the repetition rate of extreme, such as bandwidth of the system, shows the efficacy of suggested techniques and possibility to be used in military security purposes.

This paper presents a numerical optimization technique and switching phase control technique aiming at improvement of efficiency of the low voltage BLDC motor. The optimization technique is performed using the generalized sensitivity technique, response surface method(RSM) and sampling minimization technique. In order to minimize current consumption of the BLDC motor, the switching method of the driving device is optimized using RSM with finite element analysis. The ratings of BLDC motor are 50 W, 24 V, 1200 rpm. As optimizing results, the input current is reduced from 2.78 to 2.51 [A] when the switching phase is shifted by -2.65 [DEG_ELC] at the rated driving speed of 1200 [rpm]. It is confirmed that the proposed method reduces the consuming current of the low voltage BLDC motor through switching phase control method using the numerical optimization method.

The purpose of this study is to evaluate the future potential value of CTE(Critical Technology Element)s that are evaluated to be low in TRA(Technology Readiness Assesment) and to present investment prioritization technologies in defense R&D(Research and Development) based on them. To do this, we used the DEA(Data Envelopment Analysis) method, which is useful in evaluating the efficiency of the organization. Specifically, we suggest a systematic framework to evaluate the future value of CTEs by setting the CTEs derived from the TRA process to DMU(Decision Making Unit)s, the cost and time required to develop each CTE as the input factor of the DEA and the effects of the development of each CTE as the output factor of the DEA respectively. We also conducted an illustrative case study on radar technologies to demonstrate the usefulness of the proposed approach.

In this paper, we consider a problem of partitioning a search area into smaller rectangular regions, so that multiple platforms can conduct search operations independently without requiring unnecessary coordination among themselves. The search area consists of cells where each cell has some prior information regarding the probability of target existence. The detection probability in particular cell is evaluated by multiplying the observation probability of the platform and the target existence probability in that cell. The total detection probability within the search area is defined as the cumulative detection probability for each cell. However, since this search area partitioning problem is NP-Hard, we decompose the problem into three sequential phases to solve this computationally intractable problem. Additionally, we discuss a special case of this problem, which can provide an optimal analytic solution. We also examine the performance of the proposed approach by comparing our results with the optimal analytic solution.

This study first analyzes the necessity and the validity of procuring nuclear-powered submarines, and presents an optimization model as an integer program to determine a minimum required level of them. For an optimization model, we characterize a submarine's mission, ability and availability, and apply these to the model by constraints. Then, we assign the submarines available currently and the nuclear-powered submarines, that will be newly introduced, to the predefined missions over the planning time periods in a way that the number of nuclear-powered submarines be minimized. Randomly generated missions are employed to solve a mission assignment problem, and the results show that our integer programming model provides an optimal solution as designed, and this can provide a guideline for other weapon system procurement processes.

Psychological stress and physical fatigue, such as anxiety, fear, sleep disturbance, etc., caused by exposure to the war, can lead to post-traumatic stress syndrome(PTSD) or war syndrome. The military has also prepared for drug use to minimize war syndrome and preserve combat strength. However, efforts to prevent war syndrome are still lacking. This study was conducted to identify biomarkers that can track psychophysiological changes. Psychophysiological changes associated with PTSD can be divided into four main categories. The four categories are behavioral changes, changes in brain cognition, neuroimmunological changes, and changes in innate immunity. This study suggest that biomarker profile can be made by the distance moved and the anxiety-like behavior in the open field for behavior category, brain BDNF levels in the brain cognition category, serum corticosterone in the neuroimmunology category, and inflammatory cytokine levels in the innate immunity category.