• 대기
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• 제33권5호
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• pp.457-475
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• 2023

Accurately predicting localized heavy rainfall is challenging without high-resolution mesoscale cloud information in the numerical model's initial field, as precipitation intensity and amount vary significantly across regions. In the Korean Peninsula, the radar observation network covers the entire country, providing high-resolution data on hydrometeors which is suitable for data assimilation (DA). During the pre-processing stage, radar reflectivity is classified into hydrometeors (e.g., rain, snow, graupel) using the background temperature field. The mixing ratio of each hydrometeor is converted and inputted into a numerical model. Moreover, assimilating saturated water vapor mixing ratio and decomposing radar radial velocity into a three-dimensional wind vector improves the atmospheric dynamic field. This study presents radar DA experiments using a numerical prediction model to enhance the wind, water vapor, and hydrometeor mixing ratio information. The impact of radar DA on precipitation prediction is analyzed separately for each radar component. Assimilating radial velocity improves the dynamic field, while assimilating hydrometeor mixing ratio reduces the spin-up period in cloud microphysical processes, simulating initial precipitation growth. Assimilating water vapor mixing ratio further captures a moist atmospheric environment, maintaining continuous growth of hydrometeors, resulting in concentrated heavy rainfall. Overall, the radar DA experiment showed a 32.78% improvement in precipitation forecast accuracy compared to experiments without DA across four cases. Further research in related fields is necessary to improve predictions of mesoscale heavy rainfall in South Korea, mitigating its impact on human life and property.

• 한국항행학회논문지
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• 제18권1호
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• pp.29-34
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• 2014

데이터 융합 기술은 단일 독립 레이더에 의해 이루어지는 것보다 더 정확한 추정치들을 갖기 위해 다중 레이더와 관련 정보로부터 데이터를 결합한다. 본 논문에서는 다중 레이더에서 처리되는 패킷의 지연 시간 및 손실을 분석하여 다중 레이더 데이터 융합시 중앙 자료처리 연산부에서 자료 처리 인터벌을 최소화한다. 이를 위하여 중앙 집중형 자료융합에 대한 레이더 네트워크를 모델링하고, NS-2를 이용하여 각각의 큐를 M/M/1/K로 가정하고 큐 내부에서의 패킷 지연시간과 패킷 손실을 분석한다. 분석 자료를 통해 다중 레이더 자료를 융합처리 할 때 평균 지연시간을 확인 하였으며, 이 지연시간은 융합센터에서의 레이더 자료 대기시간 기준으로 사용될 수 있다.

• 한국전자파학회논문지
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• 제30권2호
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• pp.132-140
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• 2019

이 논문에서는 수신된 레이다 신호의 특징 파라미터 데이터에 기계학습 방법을 적용하여 위협 형태에 따라 레이다 신호를 분류하는 방법을 제시한다. 현재 군에서는 위협 신호를 파악하기 위해 특징 파라미터값들과 위협 형태의 대응관계를 나타내는 라이브러리를 이용한다. 라이브러리를 이용한 방법은 새로운 위협이나 기존 라이브러리에 존재하지 않는 위협 형태에 대해서 레이다 신호를 분류하기 어렵고 위협 형태를 파악하는데 문제가 있다. 이 논문에서는 라이브러리 없이 특징 파라미터 데이터만을 이용하여 위협 형태에 따라 레이다 신호를 분류하는 방법을 제안하고자 한다. 분류기로는 CNN(convolutional neural network)을 사용하며, 기계학습을 적용하여 훈련시킨다. 제안 방법은 라이브러리를 사용하지 않음으로써 새로운 위협 신호나 기존의 라이브러리에 존재하지 않는 위협 신호도 적응적으로 분류할 수 있다.

• Journal of Astronomy and Space Sciences
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• 제34권4호
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• pp.303-314
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• 2017

With increased human activity in space, the risk of re-entry and collision between space objects is constantly increasing. Hence, the need for space situational awareness (SSA) programs has been acknowledged by many experienced space agencies. Optical and radar sensors, which enable the surveillance and tracking of space objects, are the most important technical components of SSA systems. In particular, combinations of radar systems and optical sensor networks play an outstanding role in SSA programs. At present, Korea operates the optical wide field patrol network (OWL-Net), the only optical system for tracking space objects. However, due to their dependence on weather conditions and observation time, it is not reasonable to use optical systems alone for SSA initiatives, as they have limited operational availability. Therefore, the strategies for developing radar systems should be considered for an efficient SSA system using currently available technology. The purpose of this paper is to analyze the performance of a radar system in detecting and tracking space objects. With the radar system investigated, the minimum sensitivity is defined as detection of a $1-m^2$ radar cross section (RCS) at an altitude of 2,000 km, with operating frequencies in the L, S, C, X or Ku-band. The results of power budget analysis showed that the maximum detection range of 2,000 km, which includes the low earth orbit (LEO) environment, can be achieved with a transmission power of 900 kW, transmit and receive antenna gains of 40 dB and 43 dB, respectively, a pulse width of 2 ms, and a signal processing gain of 13.3 dB, at a frequency of 1.3 GHz. We defined the key parameters of the radar following a performance analysis of the system. This research can thus provide guidelines for the conceptual design of radar systems for national SSA initiatives.

• 전기학회논문지
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• 제62권12호
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• pp.1813-1819
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• 2013

Existing level crossing obstacle detecting system was installed using a laser beam. Level crossing obstacle detecting system using a laser beam that has been a problem in relation to safety and maintainability failure according to weather conditions. We proposed laser radar level crossing obstacle detecting system as a way to overcome problem, and we developed an algorithm for this. Level crossing obstacle detecting system using a laser radar sensor algorithm is robust to external environment and a shadow zone does not exist. Sensor part of the laser radar level crossing obstacle detecting system of these is made up by the image processing unit and laser radar sensor, it operations by receiving train entering information from the control unit. In this paper, we proposed a detecting algorithm with calculation of the size of the laser radar sensor. Based on this, we were performance test on the basis of the scenario by making a prototype. In the future, laser radar level crossing obstacle detecting system to ensure the safety and reliability through the field test.

• 전기전자학회논문지
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• 제26권3호
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• pp.364-372
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• 2022

본 논문에서는 FMCW(frequency modulated continuous wave) 레이다 센서를 활용한 사람과 사물을 분류하는 시스템 설계 및 구현 결과를 제시한다. 해당 시스템은 다중 객체 탐지를 위한 레이다 센서 신호처리 과정과 객체를 사람 및 사물로 분류하는 딥러닝 과정을 수행한다. 딥러닝의 경우 높은 연산량과 많은 양의 메모리를 요구하기 때문에 경량화가 필수적이다. 따라서 CNN (convolution neural network) 연산을 이진화하여 동작하는 BNN (binary neural network) 구조를 적용하였으며, 실시간 동작을 위해 하드웨어 가속기를 설계하고 FPGA 보드 상에서 구현 및 검증하였다. 성능 평가 및 검증 결과 90.5%의 다중 객체 구분 정확도, CNN 대비 96.87% 감소된 메모리 구현이 가능하며, 총 수행 시간은 5ms로 실시간 동작이 가능함을 확인하였다.

• Journal of electromagnetic engineering and science
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• 제14권4호
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• pp.405-410
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• 2014

In this paper, we compare the quality of images reconstructed by a conventional delayed-sum (DS) algorithm and radiation pattern-based DS algorithm. In order to evaluate the quality of images, we apply the target-to-clutter ratio (TCR), which is commonly used in synthetic aperture radar (SAR) image assessment. The radiation pattern-based DS algorithm enhances the TCR of the image by focusing the target signals and preventing contamination of the radar scene. We first consider synthetic data obtained through GprMax2D/3D, a finite-difference time-domain (FDTD) forward solver. Experimental data of a 2-GHz bandwidth stepped-frequency signal are collected using a vector network analyzer (VNA) in an anechoic chamber setup. The radiation pattern-based DS algorithm shows a 6.7-dB higher TCR compared to the conventional DS algorithm.

• 대기
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• 제25권2호
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• pp.283-294
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• 2015

For urban flash flood simulation, we need the higher resolution radar rainfall than radar rainfall of KMA, which has 10 min time and 1km spatial resolution, because the area of subbasins is almost below $1km^2$. Moreover, we have to secure the high quantitative accuracy for considering the urban hydrological model that is sensitive to rainfall input. In this study, we developed the quantitative precipitation estimation (QPE), which has 250 m spatial resolution and high accuracy using KMA AWS and SK Planet stations with Mt. Gwangdeok radar data in Seoul area. As the results, the rainfall field using KMA AWS (QPE1) is showed high smoothing effect and the rainfall field using Mt. Gwangdeok radar is lower estimated than other rainfall fields. The rainfall field using KMA AWS and SK Planet (QPE2) and conditional merged rainfall field (QPE4) has high quantitative accuracy. In addition, they have small smoothed area and well displayed the spatial variation of rainfall distribution. In particular, the quantitative accuracy of QPE4 is slightly less than QPE2, but it has been simulated well the non-homogeneity of the spatial distribution of rainfall.

• 한국정보통신학회논문지
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• 제4권5호
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• pp.971-976
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• 2000

The main objective of this paper is to present the rain cell size distribution observed during squall line episodes in the Sudano-Sahelian region. The used data were collected during the EPSAT Program [Etude des Precipitation par SATellite (Satellites Study of Precipitation)] which has been developed since 1958, on an experimental area located near Niamey, Niger (2 10′32"E, 13 28′38"N). The data were obtained with a C-band radar and a network composed of approximately 100 raingages over a 10,000 $\textrm{km}^2$. In this work a culling of the squall line episodes was made for the 1992 rainy season. After radar data calibration using the raingage network a number of PPI (Plan Position Indicator) images were generated. Each image was then treated in order to obtain a series of radar reflectivity (Z) maps. To describe the cell distribution, a contouring program was used to analyze the areas with rain rate greater than or equal to the contour threshold (R$\geq$$\tau$). 24700 contours were generated, where each iso-pleth belongs to a predefined threshold. Computing each cell surface and relating its area to an equi-circle (a circle having the same area as the cell), a statistical analysis was made. The results show that the number of rain cells having a given size is an inverse exponential function of the equivalent radius. The average and median equivalent radii ate 1.4 and 0.69 In respectively. Implications of these results for the precipitation estimation using threshold methods are discussed.

• 한국해양공학회지
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• 제38권3호
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• pp.103-114
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• 2024

Remote sensing wave observation data are crucial when analyzing ocean waves, the main external force of coastal disasters. Nevertheless, it has limitations in accuracy when used in low-wind environments. Therefore, this study collected the raw data from MIROS Wave and Current Radar (MWR) and wave radar at the Ieodo Ocean Research Station (IORS) and applied the optimal filter by combining filters provided by MIROS software. The data were validated by a comparison with South Jeju ocean buoy data. The results showed it maintained accuracy for significant wave height, but errors were observed in significant wave periods and extreme waves. Hence, this study used an artificial neural network (ANN) to improve these errors. The ANN was generalized by separating the data into training and test datasets through stratified sampling, and the optimal model structure was derived by adjusting the hyperparameters. The application of ANN effectively improved the accuracy in significant wave periods and high wave conditions. Consequently, this study reproduced past wave data by enhancing the reliability of the MWR, contributing to understanding wave generation and propagation in storm conditions, and improving the accuracy of wave prediction. On the other hand, errors persisted under high wave conditions because of wave shadow effects, necessitating more data collection and future research.