• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.166-166
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• 2020

Recently, As the frequency of localized heavy rains increases, the use of high-resolution radar data is increasing. The produced radar rainfall has still gaps of spatial and temporal compared to gauge observation rainfall, and in many studies, various statistical techniques are performed for correct rainfall. In this study, the precipitation correction of the S-band Dual Polarization radar in use in the flood forecast was performed using the ConvAE algorithm, one of the Convolutional Neural Network. The ConvAE model was trained based on radar data sets having a 10-min temporal resolution: radar rainfall data, gauge rainfall data for 790minutes(July 2017 in Cheongju flood event). As a result of the validation of corrected radar rainfall were reduced gaps compared to gauge rainfall and the spatial correction was also performed. Therefore, it is judged that the corrected radar rainfall using ConvAE will increase the reliability of the gridded rainfall data used in various physically-based distributed hydrodynamic models.

• Journal of Navigation and Port Research
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• v.37 no.5
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• pp.447-452
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• 2013

Vessel traffic system uses multiple sea surveillance radars as a primary sensor to obtain maritime traffic information like as ship's position, speed, course. The systematic errors such as the range bias and the azimuth bias of the two-dimensional radar system can significantly degrade the accuracy of the radar image and target tracking information. Therefore, the systematic errors of the radar system should be corrected precisely in order to provide the accurate target information in the vessel traffic system. In this paper, it is proposed that the method compensates the range bias and the azimuth bias using AtoN information installed at VTS coverage. The radar measurement residual error model is derived from the standard error model of two-dimensional radar measurements and the position information of AtoN, and then the linear Kalman filter is designed for estimation of the systematic errors of the radar system. The proposed method is validated via Monte-Carlo runs. Also, the convergence characteristics of the designed filter and the accuracy of the systematic error estimates according to the number of AtoN information are analyzed.

• Journal of the Korea Institute of Military Science and Technology
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• v.6 no.4
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• pp.80-88
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• 2003

Radar signal simulation is increasingly gaining in importance according as modem radar systems are more complex. Although computer performance has been advanced, it is difficult to implement the real-time simulation because the detailed model for the radar is necessary to get the desired accuracy. In order to achieve real time operation, we propose radar signal generation technique using ambiguity function, Instead of wellknown correlation method. The ambiguity function is the mathematical modeling of the signal processing procedure which is a simulation section to require the most computations.

• Journal of Applied Reliability
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• v.18 no.2
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• pp.184-191
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• 2018

Purpose: The purpose of this study is to propose a method to calculate the optimal preventive maintenance cycle of radar used in the aviation weapon system of ROKAF. Methods: A hybrid model is used to estimate the optimal preventive maintenance cycle in a system that can perform condition based predictive maintenance (CBPM) through continuous diagnosis. The failure data of the radars operating in the military were used to calculate the reliability. Results: According to the research results, the reliability threshold of the radar began to decrease after 5 flights, and decreased rapidly after 12 flights. Since the second check, costs have continued to decline. Conclusion: A method is proposed to determine the cycle of optimal preventive maintenance of radar within operational budget through modeling results between reliability limit and cost for radar. The results can be used to determine the optimal preventive maintenance cycle and frequency of various avionics equipment.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.6
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• pp.319-325
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• 2023

In this study, we proposed a method for hyperparameter optimization in the building and training of a deep learning model designed to process point cloud data collected by a millimeter-wave radar system. The primary aim of this study is to facilitate the deployment of a baseline model in resource-constrained IoT devices. We evaluated a RadHAR baseline deep learning model trained on a public dataset composed of point clouds representing five distinct human activities. Additionally, we introduced a coarse-to-fine hyperparameter optimization procedure, showing substantial potential to enhance model efficiency without compromising predictive performance. Experimental results show the feasibility of significantly reducing model size without adversely impacting performance. Specifically, the optimized model demonstrated a 3.3% improvement in classification accuracy despite a 16.8% reduction in number of parameters compared th the baseline model. In conclusion, this research offers valuable insights for the development of deep learning models for resource-constrained IoT devices, underscoring the potential of hyperparameter optimization and model size reduction strategies. This work contributes to enhancing the practicality and usability of deep learning models in real-world environments, where high levels of accuracy and efficiency in data processing and classification tasks are required.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.129-143
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• 2014

The purpose of this study is to evaluate the availability of dual-polarization radar rain for flood routing in Chungju Dam watershed($6,625.8km^2$) using KIMSTORM (Grid-based KIneMatic wave STOrm Runoff Model). The Sobaek dual-polarization radar data for 1 heavy rain and 3 typhoon(Khanun, Bolaven, and Sanba) events in 2012 were obtained from Han River Flood Control Office. The spatio-temporal patterns between the two data were similar showing the ratio of radar rain to ground rain with 0.97. The KIMSTORM was set to $500{\times}500m$ resolution and a total of 45,738 cells(198 rows${\times}$231 columns) for the watershed. For radar rain and 41 ground rains, the model was independently calibrated using discharge data at 3 streamflow gauging stations(YW1, YC, and CJD) with coefficient of determination($R^2$), Nash and Sutcliffe Model Efficiency(ME), and Volume Conservation Index(VCI). The $R^2$, ME, and VCI 0.80, 0.62 and 1.08 for radar rain and 0.83, 0.68 and 1.10 for ground rain respectively.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.1
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• pp.23-32
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• 2008

According to recent frequent local flash flood due to climate change, the very short-term rainfall forecast using remotely sensed rainfall like radar is necessary to establish. This research is to evaluate the feasibility of GIS-based distributed model coupled with radar rainfall, which can express temporal and spatial distribution, for multipurpose dam operation during flood season. $Vflo^{TM}$ model was used as physically based distributed hydrologic model. The study area was Yongdam dam basin ($930\;km^2$) and the 3 storm events of local convective rainfall in August 2005, and the typhoon.Ewiniar.and.Bilis.collected from Jindo radar was adopted for runoff simulation. Distributed rainfall consistent with hydrologic model grid resolution was generated by using K-RainVieux, pre-processor program for radar rainfall. The local bias correction for original radar rainfall shows reasonable results of which the percent error from the gauge observation is less than 2% and the bias value is $0.886{\sim}0.908$. The parameters for the $Vflo^{TM}$ were estimated from basic GIS data such as DEM, land cover and soil map. As a result of the 3 events of multiple peak hydrographs, the bias of total accumulated runoff and peak flow is less than 20%, which can provide a reasonable base for building operational real-time short-term rainfall-runoff forecast system.

• Journal of Korea Water Resources Association
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• v.48 no.4
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• pp.257-268
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• 2015

The objective of this study is to assess the dual-polarization radar for flood forecasting. First, radar rainfall has temporal and spatial errors, so estimated radar rainfall was compared with ground observation rainfall to assess accuracy improvement, especially, considering the radar range of observation and increase of the rainfall intensity. The results of this study showed that the error for estimated dual-polarization radar rainfall was less than single-polarization radar rainfall. And in this study, dual-polarization radar rainfall for flood forecasting was assessed using MAP (Mean Areal Precipitation) and SURR (Sejong University Rainfall Runoff) model in Namkang dam watershed. The results of MAP are more accurate using dual-polarization radar. And the results of runoff using dual-polarization radar rainfall showed that peak flow error was reduced approximately 12~63%, runoff volumes error was reduced by approximately 30~42%, and also the root mean square error decreased compared to the result of runoff using single-polarization radar rainfall. The results revealed that dual-polarization radar will contribute to improving the accuracy of the flood forecasting.

• Journal of Advanced Navigation Technology
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• v.10 no.2
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• pp.173-180
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• 2006

An airborne radar is an essential aviation electronic system of the aircraft to perform various missions in all weather environments. This paper presents the design, development, and test results of the multi-mode pulsed Doppler radar system test model for helicopter-borne flight test. This radar system consists of 4 LRU units, which include ANTU(Antenna Unit), TRU(Tx Rx Unit), RSDU(Radar Signal & Data Processing Unit) and DISU(Display Unit). The developed technologies include the TACCAR processor, planar array antenna, TWTA transmitter, coherent I/Q detector, digital pulse compression, DSP based Doppler FFT filtering, adaptive CFAR, IMU, and tracking capability. The design performance of the developed radar system is verified through various helicopter-borne field tests including MTD (Moving Target Detector) capability for the Doppler compensation due to the moving platform motion.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.9
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• pp.693-700
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• 2018

In this paper, we propose a method to generate SAR(synthetic aperture radar) images for bistatic radar. The bistatic SAR can overcome several limitations of monostatic SAR, because the former can be applied to a variety of scenarios, compared to the latter. However, no study has been conducted on bistatic SAR imaging so far. In this paper, we propose a method to generate bistatic SAR images using the monostatic equivalent model and conventional monostatic SAR imaging algorithms. Simulations using airborne SAR in the bistatic geometry validated the efficacy of the proposed method.