• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.469-476
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• 2021

An electronic attack (EA) system is an essential weapon system for performing electronic warfare missions that contain signal tracking and jamming against multiple threats using electromagnetic waves, such as air defense radars, wireless command and communication networks, and guided missiles. The combat effectiveness can be maximized, and the survivability of militarily protecting combat power can be enhanced through EA mission operations, such as disabling the functions of multiple threats. The EA system can be used as a radio frequency jamming system to respond to drone attacks on the core infrastructure, such as airports, power plants, and communication broadcasting systems, in the civilian field. This study examined the criteria for classification according to the electronic attack missions of foreign EA systems based on an aviation platform. The foreign R&D trends by those criteria were investigated. Moreover, by analyzing the R&D trends of domestic EA systems and future battlefields in the domestic security environments, this paper proposes technological development plans of EA systems suitable for the future battlefield environments compared to the foreign R&D trends.

• Geophysics and Geophysical Exploration
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• v.8 no.4
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• pp.270-279
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• 2005

Under the research project supported by Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), we have conducted the development of GPR systems for landmine detection. Until 2005, we have finished development of two prototype GPR systems, namely ALIS (Advanced Landmine Imaging System) and SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar). ALIS is a novel landmine detection sensor system combined with a metal detector and GPR. This is a hand-held equipment, which has a sensor position tracking system, and can visualize the sensor output in real time. In order to achieve the sensor tracking system, ALIS needs only one CCD camera attached on the sensor handle. The CCD image is superimposed with the GPR and metal detector signal, and the detection and identification of buried targets is quite easy and reliable. Field evaluation test of ALIS was conducted in December 2004 in Afghanistan, and we demonstrated that it can detect buried antipersonnel landmines, and can also discriminate metal fragments from landmines. SAR-GPR (Synthetic Aperture Radar-Ground Penetrating Radar) is a machine mounted sensor system composed of B GPR and a metal detector. The GPR employs an array antenna for advanced signal processing for better subsurface imaging. SAR-GPR combined with synthetic aperture radar algorithm, can suppress clutter and can image buried objects in strongly inhomogeneous material. SAR-GPR is a stepped frequency radar system, whose RF component is a newly developed compact vector network analyzers. The size of the system is 30cm x 30cm x 30 cm, composed from six Vivaldi antennas and three vector network analyzers. The weight of the system is 17 kg, and it can be mounted on a robotic arm on a small unmanned vehicle. The field test of this system was carried out in March 2005 in Japan.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.925-938
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• 2022

Agricultural reservoirs are an important water resource nationwide and vulnerable to abnormal climate effects such as drought caused by climate change. Therefore, it is required enhanced management for appropriate operation. Although water-level tracking is necessary through continuous monitoring, it is challenging to measure and observe on-site due to practical problems. This study presents an objective comparison between multiple AI models for water-body extraction using radar images that have the advantages of wide coverage, and frequent revisit time. The proposed methods in this study used Sentinel-1 Synthetic Aperture Radar (SAR) images, and unlike common methods of water extraction based on optical images, they are suitable for long-term monitoring because they are less affected by the weather conditions. We built four AI models such as Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN), and Automated Machine Learning (AutoML) using drone images, sentinel-1 SAR and DSM data. There are total of 22 reservoirs of less than 1 million tons for the study, including small and medium-sized reservoirs with an effective storage capacity of less than 300,000 tons. 45 images from 22 reservoirs were used for model training and verification, and the results show that the AutoML model was 0.01 to 0.03 better in the water Intersection over Union (IoU) than the other three models, with Accuracy=0.92 and mIoU=0.81 in a test. As the result, AutoML performed as well as the classical machine learning methods and it is expected that the applicability of the water-body extraction technique by AutoML to monitor reservoirs automatically.

• Journal of Korea Water Resources Association
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• v.55 no.12
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• pp.1125-1135
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• 2022

Damages by heavy snow does not occur very often, but when it does, it causes damage to a wide area. To mitigate snow damage, it is necessary to know, in advance, the depth of snow that causes damage in each region. However, snow depths are measured at observatory locations, and it is difficult to understand the spatial distribution of snow depth that causes damage in a region. To understand the spatial distribution of snow depth, the point measurements are interpolated. However, estimating spatial distribution of snow depth is not easy when the number of measured snow depth is small and topographical characteristics such as altitude are not similar. To overcome this limit, satellite images such as Synthetic Aperture Radar (SAR) can be analyzed using Differential Interferometric SAR (DInSAR) method. DInSAR uses two different SAR images measured at two different times, and is generally used to track minor changes in topography. In this study, the spatial distribution of snow depth was estimated by DInSAR analysis using dual polarimetric IW mode C-band SAR data of Sentinel-1B satellite operated by the European Space Agency (ESA). In addition, snow depth was estimated using geostationary satellite Chollian-2 (GK-2A) to compare with the snow depth from DInSAR method. As a result, the accuracy of snow cover estimation in terms with grids was about 0.92% for DInSAR and about 0.71% for GK-2A, indicating high applicability of DInSAR method. Although there were cases of overestimation of the snow depth, sufficient information was provided for estimating the spatial distribution of the snow depth. And this will be helpful in understanding regional damage-causing snow depth.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.4
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• pp.552-557
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• 2020

The Radar Cross Section (RCS) is a virtual region indicating the strength of a wavelength at which a radar signal is reflected and received. As the ship's RCS represents its own stealth performance and survivability, efforts have been made in various areas from design to construction to reduce the RCS. The RCS can be predicted using design drawings and CAD models, but it is necessary to measure the RCS at sea since sea clutter and multipath reflections occur in the sea environment. However, such RCS predictions and measured values provide only a simple relative magnitude to the user, and there has not been much research on this topic. In this paper, a missile countermeasure technique was studied using 3D RCS measurement data in an operating environment. The elevation and azimuth angle of the ship viewed from the missile were estimated using the location information of the missile, and the RCS value was inverted by mapping it to previously measured 3D RCS measurement data. In addition, by using the movement information of the missile, the RCS observed by the missile could be predicted in advance, and this method can be used to propose a response plan based on the maneuvering and chaff system.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.4
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• pp.285-293
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• 2020

In this paper, to improve Synthetic Aperture Radar (SAR) image quality, the effect of time synchronization error in the EGI/IMU (Embedded GPS/INS, Inertial Measurement Unit) integrated system is analyzed and state augmentation is applied to compensate it. EGI/IMU integrated system is widely used as a SAR motion measurement algorithm, which consists of EGI mounted to obtain the trajectory and IMU mounted on the SAR antenna. In an EGI/IMU integrated system, a time synchronization error occurs when the clocks of the sensors are not synchronized. Analysis of the effect of time synchronization error on navigation solutions and SAR images confirmed that the time synchronization error deteriorates SAR image quality. The state augmentation is applied to compensate for this and as a result, the SAR image quality does not decrease. In addition, by analyzing the performance and the observability of the time synchronization error according to the maneuver, it was confirmed that the time-variant maneuver such as rotational motion is necessary to estimate the time synchronization error adequately. In order to reduce the influence of the time synchronization error on the SAR image, the time synchronization error must be compensated by performing maneuver changing over time such as a rotation before SAR operation.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.5
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• pp.495-501
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• 2015

Abroad warships are confronted with various menaces. The most critical threat of the warship is an Anti-Ship Missile (ASM). The ASM is able to be launched at a variety of environments and platforms. The ASM can evades conventional naval radar systems and electronic countermeasure techniques for providing a fatal damage to the warship. To cope with the ASM, an active decoy is an effective method to minimize the direct damage to the warship. The active decoy increases survivability of the warship because the ASM can lure pursuit of the active decoy instead of the warship. In this paper, our proposed method verifies an available response time of the active decoy to deal with the ASM using the active decoy of the warship in marine environments. We defined models of the warship, the ASM, and the active decoy, and executed simulation by combining the models. By the simulation result, the proposed method demonstrated the superiority of the mobile active decoy of the response time decoy among various active decoys, and estimated a protection area to prevent the ASM according the response time of the mobile active decoy against the ASM.

• The Journal of the Acoustical Society of Korea
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• v.43 no.3
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• pp.351-360
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• 2024

With the increasing use of drone technology, the Unmanned Aerial Vehicle (UAV) is now being utilized in various fields. However, this increased use of drones has resulted in various issues. Due to its small size, the drone is difficult to detect with radar or optical equipment, so acoustical tracking methods have been recently applied. In this paper, a method of localization of multiple drones using the acoustic characteristics of the quadcopter drone is suggested. Because the acoustic characteristics induced by each rotor are differentiated depending on the type of drone and its movement state, the sound source of the drone can be reconstructed by spatially clustering the results of the estimated positions of the blade passing frequency and its harmonic sound source. The reconstructed sound sources are utilized to finally determine the location of multiple-drone sound sources by applying the source localization algorithm. An experiment is conducted to analyze the acoustic characteristics of the test quadcopter drones, and the simulations for three different types of drones are conducted to localize the multiple drones based on the measured acoustic signals. The test result shows that the location of multiple drones can be estimated by utilizing the acoustic characteristics of the drone. Also, one can see that the clarity of the separated drone sound source and the source localization algorithm affect the accuracy of the localization for multiple-drone sound sources.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.36 no.3
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• pp.175-185
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• 2000

This paper described on a basic study to organize fishing vessel control system in order to control efficiently fishing vessel in Korean offshore. It was digitalized ARPA image on the fishing processing of a fleet of purse seiner in conducting fishing operation at Cheju offshore in Korea as a digital camera and then simulated by used VTMS. Futhermore, it was investigated on the application of FVTMS which can control efficiently fishing vessels in fishing ground. The results obtained were as follows ; (1) It was taken 16 minutes and 35 minutes to casting and hauling net in fishing processing respectively. The length of rope pulled by scout boat was 200m, tactical diameter in casting net was 340.8m, turning speed was 6kts as well. (2) The processing of casting and hauling net was moved to SW, NE as results of simulation when the current direction and speed set into NE, 2kts and SW, 2kts respectively. Such as these results suggest that can predict to control the fishing vessel previously with information of fishing ground, fishery and ship's maneuvering, etc. (3) The control range of VTMS radar used in simulation was about 16 miles. Although converting from a radar of the control vessel to another one, it was continuously acquired for the vector and the target data. The optimum control position could be determined by measuring and analyzing to distance and direction between the control vessel and the fleet of fishing vessel. (4) The FVTMS(fishing vessel traffic management services) model was suggested that fishing vessels received fishing conditions and safety navigation information can operate safely and efficiently.