• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.3
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• pp.219-230
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• 2022

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.

• Journal of Korea Multimedia Society
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• v.25 no.2
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• pp.206-214
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• 2022

The way to measure the extent of damage from floods and droughts is to identify changes in the extent of water systems. In order to effectively grasp this at a glance, satellite images are used. KOMPSAT-5 uses Synthetic Aperture Radar (SAR) to capture images regardless of weather conditions such as clouds and rain. In this paper, various deep learning models are applied to perform semantic segmentation of the water system in this SAR image and the performance is compared. The models used are U-net, V-Net, U2-Net, UNet 3+, PSPNet, Deeplab-V3, Deeplab-V3+ and PAN. In addition, performance comparison was performed when the data was augmented by applying elastic deformation to the existing SAR image dataset. As a result, without data augmentation, U-Net was the best with IoU of 97.25% and pixel accuracy of 98.53%. In case of data augmentation, Deeplab-V3 showed IoU of 95.15% and V-Net showed the best pixel accuracy of 96.86%.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.71-74
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• 2006

파랑은 선박의 안전운항에 있어서 중요한 요소이다. 지금까지의 파랑관측은 부이와 같은 현장관측에 주로 의존하고 있으나, 근년 위성에 의한 리모트센싱의 기술이 발달함에 따라, 파랑계측에 분야에도 시도되고 있다. 하지만 국내에서는 SAR(합성개구레이더)의 처리 경험 자체가 부족하여 본격적인 연구개발이 시작되지 않고 있다. 본 연구에서는 SAR를 이용한 파랑계측을 하기 위한 초기단계로, 1) SAR화상에서 파랑의 영상화 메커니즘, 2) 파랑 스펙트럼 추정 기법의 소개를 하고자 한다.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2002.10a
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• pp.195-199
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• 2002

SAR(Synthetic Aperture Radar) 영상은 경사촬영을 수행하므로 지형의 기복에 따른 영향을 많이 받는다. 따라서 SAR영상을 이용하여 여러 가지 정보들을 추출하여 이용하기 위해서는 전처리 과정으로서 지형의 기복에 따른 여러 가지 왜곡들을 보정해야 한다. 이에 본 연구에서는 RADARSAT SAR 영상을 이용하여 궤도모델링, 정사보정을 수행하고 역산란계수, 국부입사각 계산 등을 통해 지형기복에 따른 방사왜곡보정을 수행하였다.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.487-495
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• 2022

This paper describes the design and development of a high-power transmit receive module(TRM) for mounting on X-band synthetic aperture radar(SAR) of the NEXTSat-2. The TRM generates a high-power pulse signal with a bandwidth of 100 MHz in the target frequency range of X-band and amplifies a low-noise on the received signal. Tx. path of the TRM has output signal level of more than 200 watts (53.01 dB), pulse droop of 0.35 dB, signal strength change of 0.04 dB during transmission signal output, and phase change of 1.7 ˚. Rx. path has noise figure of 3.99 dB and gain of 37.38 ~ 37.46 dB. It was confirmed the TRM satisfies all requirements. The TRM mounted on the NEXTSat-2 flight model(FM) which will be launched using the KSLV-II (Nuri).

• Journal of Korea Water Resources Association
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• v.54 no.2
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• pp.81-91
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• 2021

Synthetic Apreture Radar (SAR) is attracting attentions with its possibility of producing high resolution data that can be used for soil moisture estimation. High resolution soil moisture data enables more specific observation of soil moisture than existing soil moisture products from other satellites. It can also be used for studies of wildfire, landslide, and flood. The SAR based soil moisture estimation should be conducted considering vegetation, which affects backscattering signals from the SAR sensor. In this study, a SAR based soil moisture estimation at regions covered with various vegetation types on the middle area of Korea (Cropland, Grassland, Forest) is conducted. The representative backscattering model, Water Cloud Model (WCM) is used for soil moisture estimation over vegetated areas. Radar Vegetation Index (RVI) and in-situ soil moisture data are used as input factors for the model. Total 6 study areas are selected for 3 vegetation types according to land cover classification with 2 sites per each vegetation type. Soil moisture evaluation result shows that the accuracy of each site stands out in the order of grassland, forest, and cropland. Forested area shows correlation coefficient value higher than 0.5 even with the most dense vegetation, while cropland shows correlation coefficient value lower than 0.3. The proper vegetation and soil moisture conditions for SAR based soil moisture estimation are suggested through the results of the study. Future study, which utilizes additional ancillary vegetation data (vegetation height, vegetation type) is thought to be necessary.

• Journal of IKEEE
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• v.27 no.3
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• pp.265-272
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• 2023

In this paper, a GPU-accelerated kernel of range Doppler algorithm (RDA) was developed for real-time image formation based on frequency modulated continuous wave (FMCW) synthetic aperture radar (SAR). A pinned memory was used to minimize the data transfer time between the host and the GPU device, and the kernel was configured to perform all RDA operations on the GPU to minimize the number of data transfers. The dataset was obtained through the FMCW drone SAR experiment, and the GPU acceleration effect was measured in an intel i7-9700K CPU, 32GB RAM, and Nvidia RTX 3090 GPU environment. Including the data transfer time between host and devices, it was measured to be accelerated up to 3.41 times compared to the CPU, and when only the acceleration effect of operation was measured without including the data transfer time, it was confirmed that it could be accelerated up to 156 times.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.6
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• pp.459-469
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• 2017

An air-to-ground guidance missile aimed to hit a main battle tank(MBT) should detect a ground moving target and estimate the target position to guide. In this paper, we detect a front ground moving target by using FMCW(Frequency Modulated Continuous Wave) and estimate the position by forward-looking SAR(Synthetic Aperture Radar) via scanning certain front ground section by steering a beam with narrow beamwidth left to right mechanically. Also, by MLE(Maximum Likelihood Estimation), degree of how fast the target approach or recede from the radar can be figured out from the estimated radial velocity of the moving target. Subsequently, we generate a radar image via corrected matched filter from phase history including the radial velocity.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.8
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• pp.636-645
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• 2017

A scanning radar is exploited widely such as for ground surveillance, disaster rescue, and etc. However, the range resolution is limited by transmitted bandwidth and cross-range resolution is limited by beam width. In this paper, we propose a method for super-resolution radar imaging. If the distribution of reflectivity is sparse, the distribution is called sparse signal. That is, the problem could be formulated as compressive sensing problem. In this paper, 2D super-resolution radar image is generated via reweighted ${\ell}_1-Minimization$. In the simulation results, we compared the images obtained by the proposed method with those of the conventional Orthogonal Matching Pursuit(OMP) and Synthetic Aperture Radar(SAR).

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.8
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• pp.624-631
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• 2018

To meet the requirements of various satellite synthetic aperture radar(SAR) system performance parameters, the characteristics of the antenna pattern should be analyzed. In this paper, we propose a method to improve the SAR system performance using an effective technique for optimizing antenna pattern synthesis in the presence of element failure. The desired antenna pattern can be synthesized by referring to the optimized antenna mask templates using the particle swarm optimization algorithm. In the simulation, the performance of the proposed method is verified by analyzing characteristics related to the SAR system performance parameters using antenna pattern regeneration.