• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.11
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• pp.1285-1294
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• 2010

This study describes the design and verification of short range UWB(Ultra Wideband) imaging radar that is able to display high resolution radar image for front area of a UGV(Unmanned Ground Vehicle). This radar can help a UGV to navigate autonomously as it detects and avoids obstacles through foliage. We describe the relationship between bandwidth of transmitting signal and range resolution. A vivaldi antenna is designed and it's radiation pattern and reflection are measured. It is easy to make array antenna because of small size and thin shape. Aperture size of receiving array antenna is determined by azimuth resolution of radar image. The relation of interval of receiving antenna array, image resolution and aliasing of target on a radar image is analyzed. A vector network analyzer is used to obtain the reflected signal and corner reflectors as targets are positioned at grass field. Applicability of the proposed radar to UGV is proved by analysis of image resolution and penetrating capability for grass in the experiment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.9
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• pp.1337-1346
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• 2015

In this paper, pattern classifier is designed to classify precipitation and non-precipitation events from weather radar data. The proposed classifier is based on Fuzzy Neural Network(FNN) and consists of three FNNs which operate in parallel. In the proposed network, the connection weights of the consequent part of fuzzy rules are expressed as two polynomial types such as constant or linear polynomial function, and their coefficients are learned by using Least Square Estimation(LSE). In addition, parametric as well as structural factors of the proposed classifier are optimized through Differential Evolution(DE) algorithm. After event classification between precipitation and non-precipitation echo, non-precipitation event is to get rid of all echo, while precipitation event including non-precipitation echo is to get rid of non-precipitation echo by classifier that is also based on Fuzzy Neural Network. Weather radar data obtained from meteorological office is to analysis and discuss performance of the proposed event and echo patter classifier, result of echo pattern classifier compare to QC(Quality Control) data obtained from meteorological office.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.111-116
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• 2008

Radar system must estimate exactly quickness and target in interference channel. Because interference of radio channel is multipath channel by artificial structure and nature structure. signal estimation is difficult. As long as, get rid of interference signal have been study digital beamforming, adaptive array antenna and so on. In this paper, proposed SPT-SALCMV beamforming algorithm get rid of coherent interference algorithm and adaptive array antenna. Adaptive array forms null pattern and reduces gains for direction of interference signal. And estimate signal that want by keeping gains of beam pattern changelessly to target signal direction. In this paper, proposed SPT-SALCMV algorithm was exactly received position of target. But general SPT-LCMV algorithm resulted beam error about 30degrees. Therefore, proved that SPT-SALCMV algerian that propose in this paper is more excellent than genaral SPT-LCMV algorithm.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.775-778
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• 2016

In this paper, we propose a new method to automatically recognize PRI modulation type of radar signal at ES(Electronic Support) in electronic singal environment. The propose method stores pattern of PRI(Pulse Repetition Interval) of radar signal and uses statistic data, which firstly classifies into 2 classes. Then the proposed method recognizes each PRI signal using statistic characteristic of PRI. We apply various 5 kinds of PRI signal such as constant PRI, jitter PRI, D&S(dwell & switch) PRI, stagger PRI, sliding PRI, etc. The result shows the proposed method correctly identifies various PRI signals.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.427-435
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• 2000

It is often crucial to obtain a map of flood inundated area with more accurate and rapid manner. This study attempts to evaluate the potential of satellite synthetic aperture radar (SAR) data for mapping of flood inundated area in Imjin river basin. Multitemporal RADARSAT SAR data of three different dates were obtained at the time of flooding on August 4 and before and after the flooding. Once the data sets were geometrically corrected and preprocessed, the temporal characteristics of relative radar backscattering were analyzed. By comparing the radar backscattering of several surface features, it was clear that the flooded rice paddy showed the distinctive temporal pattern of radar response. Flooded rice paddy showed significantly lower radar signal while the normally growing rice paddy show high radar returns, which also could be easily interpreted from the color composite imagery. In addition to delineating the flooded rice fields, the multitemporal radar imagery also allow us to distinguish the afterward condition of once-flooded rice field.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.6
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• pp.19-30
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• 2013

This study is to evaluate the use of dual-polarization radar data for storm runoff modeling in Namgang dam (2,293 $km^2$) watershed using KIMSTORM (Grid-based KIneMatic wave STOrm Runoff Model). The Bisl dual-polarization radar data for 3 typhoons (Khanun, Bolaven, Sanba) and 1 heavy rain event in 2012 were obtained from Han River Flood Control Office. Even the radar data were overall less than the ground data in areal average, the spatio-temporal pattern between the two data was good showing the coefficient of determination ($R^2$) and bias with 0.97 and 0.84 respectively. For the case of heavy rain, the radar data caught the rain passing through the ground stations. The KIMSTORM was set to $500{\times}500$ m resolution and a total of 21,372 cells (156 rows${\times}$137 columns) for the watershed. Using 28 ground rainfall data, the model was calibrated using discharge data at 5 stations with $R^2$, Nash and Sutcliffe Model Efficiency (ME) and Volume Conservation Index (VCI) with 0.85, 0.78 and 1.09 respectively. The calibration results by radar rainfall showed $R^2$, ME and VCI were 0.85, 0.79, and 1.04 respectively. The VCI by radar data was enhanced by 5 %.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.275-275
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• 2021

A fixed Z-R relationship approach, such as the Marshall-Palmer relationship, for an entire year and for different seasons can be problematic in cases where the relationship varies spatially and temporally throughout a region. From this perspective, this study explores the use of long-term radar reflectivity for South Korea to obtain a nationwide calibrated Z-R relationship and the associated uncertainties within a Bayesian regression framework. This study also investigates seasonal differences in the Z-R relationship and their roles in reducing systematic error. Distinct differences in the Z-R parameters in space are identified, and more importantly, an inverse relationship between the parameters is clearly identified with distinct regimes based on the seasons. A spatially structured pattern in the parameters exists, particularly parameter α for the wet season and parameter β for the dry season. A pronounced region of high values during the wet and dry seasons may be partially associated with storm movements in that season. Finally, the radar rainfall estimates through the calibrated Z-R relationship are compared with the existing Z-R relationships for estimating stratiform rainfall and convective rainfall. Overall, the radar rainfall fields based on the proposed modeling procedure are similar to the observed rainfall fields, whereas the radar rainfall fields obtained from the existing Marshall-Palmer Z-R relationship show a systematic underestimation. The obtained Z-R relationships are validated by testing the predictions on unseen radar-gauge pairs in the year 2018, in the context of cross-validation. The cross-validation results are largely similar to those in the calibration process, suggesting that the derived Z-R relationships fit the radar-gauge pairs reasonably well.

• Journal of Korea Water Resources Association
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• v.50 no.5
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• pp.335-346
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• 2017

This study generated the radar-based forecasted rainfall using spatial-scale decomposition method (SCDM) and evaluated the hydrological applicability with forecasted rainfall by KMA (MAPLE, KONOS) in terms of urban flood forecasting. SCDM is to separate the small-scale field (convective cell) and large-scale field (straitform cell) from radar rainfield. And each separated field is forecasted by translation model and storm tracker nowcasting model for improvement of QPF accuracy. As the evaluated results of various QPF for three rainfall events in Seoul and Metropolitan area, proposed method showed better prediction accuracy than MAPLE and KONOS considering the simplicity of the methodology. In addition, this study assessed the urban hydrological applicability for Gangnam basin. As the results, KONOS simulated the peak of water depth more accurately than MAPLE and SCDM, however cannot simulated the timeseries pattern of water depth. In the case of SCDM, the quantitative error was larger than observed water depth, but the simulated pattern was similar to observation. The SCDM will be useful information for flood forecasting if quantitative accuracy is improved through the adjustment technique and blending with NWP.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.5
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• pp.514-520
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• 2015

Sidelobe canceller(SLC) requires main beam pattern(SUM beam) and auxiliary beam patterns for rejection of sidelobe noise jammer. For best performance of sidelobe noise jamming cancellation of adaptive SLC, gain dominant region of each auxiliary beam pattern shall not be overlapped one another in elevation/azimuth regions of sidelobe of main beam, and beam patterns of auxiliary channels should have low gains in regions of mainlobe of main beam. In the monopulse radar, the difference beam patterns for monopulse processing have these properties. This paper proposes the method using data from the difference channel for monopulse processing as data from auxiliary channel for sidelobe cancellation. For the proposed SLC, the results of simulation and performance analysis was presented. If the proposed method is used in the monopulse radar, SLC can be constructed by using basic SUM and difference channels without extra channel composition.