In this paper, a design for a radio frequency(RF) and infrared(IR) absorber with metasurfaces is discussed in microwave frequency bands. The RF absorber includes double layers of metasurfaces to operate in S- and X-bands. Effects of sheet resistance of the metasurfaces and thicknesses of dielectric supporting layers on reflection responses are investigated. An IR stealth layer incorporates an array of conductive grids with slits to reflect IR signals but to transmit RF signals and visible rays. Periodicity of the grids and slits is studied for transmission responses in the X-band and a surface area ratio. Reflection responses of the RF/IR multispectral absorber are found to be lower than -10 dB and -16 dB in the S- and X-bands, respectively, from full-wave simulation. Finally, the RF/IR multispectral absorber is fabricated and its reflection responses are measured to verify designed performance.

Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.

Free-space optical communication technology enables the high-speed data transmission and excellent anti-jamming security. We conduct research on satellite-to-ground free-space optical communication links for high-speed transmission of large-capacity surveillance and reconnaissance data. Since the satellite continues to move along its orbit while the optical signal is transmitted between the satellite and the ground, the pointing angle of the beam from the ground terminal needs to be corrected by Point Ahead Angle(PAA) so that the transmitted light reaches the expected location of the satellite. In this paper, we present the algorithm for PAA estimation and control.

Magnetic anomaly signals from the ferromagnetic targets such as ships in the sea are measured by drone-based magnetic anomaly detection. A quantum magnetometer is suspended from the drone by 4 strings. Flight altitude and speed of drone are 100 m and 5 m/s, respectively. We obtain magnetic anomaly signals of few nT from the ships clearly. We analyze the signal characteristics by the ferromagnetic target through simulation using COMSOL multiphysics.

Deep learning models require high computing power due to a substantial amount of computation. It is difficult to use them in devices with limited computing environments, such as coastal surveillance equipments. In this study, a lightweight model is constructed by analyzing the weight changes of the convolutional layers during the training process based on MobileNet and then pruning the layers that affects the model less. The performance comparison results show that the lightweight model maintains performance while reducing computational load, parameters, model size, and data processing speed. As a result of this study, an effective pruning method for constructing lightweight deep learning models and the possibility of using equipment resources efficiently through lightweight models in limited computing environments such as coastal surveillance equipments are presented.

This study proposes a method for determining the optimal target size for an artillery range considering ballistics and environmental conditions. To this end, the size of the probable error of each type of ammunition and charge determined during shooting were considered, and the effect of the firing position and target terrain characteristics on the target size was analyzed. In conclusion, the size of the target increased as the range increased, and a larger target size was required for the DPICM than for the general high explosive. Accordingly, the optimal target size must be determined by considering various factors such as topographical characteristics, shooting position location, and shooting range safety standards.

Today, risks created by uncertainty must be managed for successful project execution. From this perspective, applying a risk management process is very important for successful defense systems test works. This paper describes 'the implentation of risk management process for test work' carried out by DTERI's process improvement activities. In this study, the concept of risk management process, and details of the risk management process are examined through PMBOK and ISO/IEC/IEEE 15288, CMMI. After that, we defined 'Standard Process for Risk Management' of defence systems test works. And, we describe 'Risk Management Function' of DTERI's Project Management System(PMS) and the risk management process of DTERI. Finally, the effectiveness of the risk management standard process is verified through quantitative analysis.

Autonomous weapon systems act according to artificial intelligence-based judgement based on recognition through various sensors. Test and evaluation for various scenarios is required depending on the characteristics that artificial intelligence-based judgement is made. As a part of this approach, this paper proposed a LiDAR point cloud augmentation method for mixed-reality based test and evaluation. The augmentation process is achieved by mixing real and virtual LiDAR signals based on the virtual LiDAR synchronized with the pose of the autonomous weapon system. For realistic augmentation of test and evaluation purposes, appropriate intensity values were inserted when generating a point cloud of a virtual object and its validity was verified. In addition, when mixing the generated point cloud of the virtual object with the real point cloud, the proposed method enhances realism by considering the occlusion phenomenon caused by the insertion of the virtual object.

This paper describes a progressive test and evaluation strategy for verification of Korean Fighter eXperimental (KF-X) AESA(Active Electronically Scanned Array) radar development. Three progressive stages of development test and evaluation were officially performed from simulated test conditions to actual operating conditions according to standards: radar function/performance and avionics integration. KF-X AESA radar development is repeatedly verified by progressive stages consisting of five tests: Roof-lab ground test, System Integration Laboratory(SIL) ground test, Flying Test Bed(FTB) test, KF-X ground test, and KF-X flight test. As a result, the risk factor decreases as stages and tests progress. Therefore, development test and evaluation of KF-X AESA radar are successfully performed at low development risk.

This study provides a thorough examination of Unmanned Ground Vehicles(UGVs), focusing on crucial technologies and trends across major global markets. It includes an in-depth patent analysis revealing the dominant positions of the United States and the European Union in this field. Additionally, it underscores substantial advancements made by China, Japan, and Korea since 2010. Using Latent Dirichlet Allocation(LDA)-based patent text mining, the study identified key technology areas in UGV development, such as advanced control systems, navigation technologies, power supply mechanisms, and sensing and communication tools. Through linear regression analysis, the study predicted the future paths of these technology areas, offering important insights into the evolving world of UGV technology. The findings can provide strategic guidance for stakeholders in the defense, commercial, and academic sectors, pointing out the future directions in UGV advancements.

As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.

The wake-homing underwater guided weapon that detects and tracks wake generated during voyage of a surface ship is impossible to avoid with the present acoustic deception torpedo defense system. Therefore, research on bubble-generating wake decoy is necessary to deceive wake-homing underwater guided weapon. Experiments in various environments are required to verify the effective operation method and performance of the wake decoy, but performance verification through underwater experiment is limited. In this paper, we develop a simulator for an torpedo defense system of surface ship, which is applied bubble-generating wake decoy, against acoustic, wake, and hybrid homing underwater guided weapon attack. The simulator includes surface ship model, acoustic decoy(static, mobile) model, bubble-generating wake decoy model, search and motion model of underwater guided weapon and so on. By integrating various models, MATLAB GUI simulator was developed. Through the simulation results for various environmental variables by this simulator, it is judged that effective operation method and performance verification of the bubble-generating wake decoy can be performed.

The important factors in the design of the gun propellant are impetus, flame temperature and pressure. In this paper, we considered a nitrocellulose based propellant composition that replaced sensitive NG(Nitroglycerin) with RDX(Cyclotrimethylenetrinitramine) and DEGDN(Diethylene glycol dinitrate) which high energy and low sensitivity. Particle size and content of RDX are the two main factors that affect the burning stability of RDX-based propellants. Among them, the characteristics of the propellant according to the particle size of RDX were confirmed. The relative combustion rate(R.Q., Relative Quickness) of the propellant changed according to the RDX particle size, and internal ballistics of properties of propellant were also varied. The particle size of RDX can be confirmed as a major factor in the combustion and internal ballistics characteristics of the propellant.