• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.6
• /
• pp.822-834
• /
• 2017

In M&S, radar model is a software module to identify position data of simulation objects. In this paper, we propose a radar performance model for simulations of air defenses. The previous radar simulations are complicated and difficult to model and implement since radar systems in real world themselves require a lot of considerations and computation time. Moreover, the previous radar simulations completely depended on radar equations in academic fields; therefore, there are differences between data from radar equations and data from real world in mission level analyses. In order to solve these problems, we firstly define functionality of radar systems for air defense. Then, we design and implement the radar performance model that is a simple model and deals with being independent from the radar equations in engineering levels of M&S. With our radar performance model, we focus on analyses of missions in our missile model and being operated in measured data in real world in order to make sure of reliability of our mission analysis as much as it is possible. In this paper, we have conducted case studies, and we identified the practicality of our radar performance model.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2000.10b
• /
• pp.1279-1284
• /
• 2000

This study introduces a simulation model of radar responses with frequencies on subsurface voids in concrete. In this model, the resolution and the attenuation according to radar frequencies in each interface which has different electromagnetic property are analyzed. This model aims to select the best frequency of radar which can analyze the thickness of voids in concrete from radar response. It also can be applied to estimate the limitation of propagation depth of radar on subsurface voids in concrete. The computed results show the radar images based on radar signal processing using convolution technique.

• Proceedings of the KSRS Conference
• /
• 2003.11a
• /
• pp.41-43
• /
• 2003

A radar scattering model is developed based on an empirical rough surface scattering model, the radiative transfer model (RTM), a numerical simulation algorithm of radar scattering from particles, and experimental data obtained by ground-based scatterometers and SAR systems. At first, the scattering matrices of scattering particles such as a leaf, a branch, and a trunk, have been modeled using the physical optics (PO) model and the numerical full-wave analysis. Then, radar scattering from a group of mixed particles has been modeled using the RTM, which leads to a general scattering model for earth surfaces. Finally, the scattering model has been verified with the experimental data obtained by scatterometers and SAR systems.

• Proceedings of the IEEK Conference
• /
• 2002.06a
• /
• pp.195-198
• /
• 2002

One-dimensional radar signature, such as range profile, is highly dependent on the aspect angle. Therefore, radar target recognition over wide angular region is a very difficult task. In this paper, we propose the Bayes classifier with Gaussian mixture model for radar target recognition over wide-angular region and compare performances of proposed technique and radar target recognition with subclasses concept in the literature of probability of correct classification ratio.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.19 no.4
• /
• pp.443-452
• /
• 2016

Current low-altitude radar system often fails to detect small unmanned aerial vehicles (UAV) because of their small radar cross section (RCS) compared with larger targets. As a potential alternative, a passive bistatic radar system has been considered. We study an optimal deployment problem for the passive bistatic radar system. We model this problem as a covering problem, and develop an integer programming model. The objective of the model is to maximize coverage of a passive bistatic radar system. Our model takes into account factors specific to a bistatic radar system, including bistatic RCS and transmitter-receiver pair coverage. Considering bistatic RCS instead of constant RCS is important because the slight difference of RCS value for small UAVs could significantly influence the detection probability. The paired radar coverage is defined by using the concept of gradual coverage and cooperative coverage to represent a realistic environment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.39 no.5
• /
• pp.253-263
• /
• 2021

Traffic data collected using advanced equipment are highly valuable for traffic planning and efficient road operation. However, there is a problem regarding the reliability of the analysis results due to equipment defects, errors in the data aggregation process, and missing data. Unlike other detectors installed for each vehicle lane, radar detectors can yield different error types because they detect all traffic volume in multilane two-way roads via a single installation external to the roadway. For the traffic data of a radar detector to be representative of reliable data, the error factors of the radar detector must be analyzed. This study presents a field survey of variables that may cause errors in traffic volume collection by targeting the points where radar detectors are installed. Video traffic data are used to determine the errors in traffic measured by a radar detector. This study establishes three types of radar detector traffic errors, i.e., artificial, mechanical, and complex errors. Among these types, it is difficult to determine the cause of the errors due to several complex factors. To solve this problem, this study developed a radar detector traffic volume error analysis model using a multiple linear regression model. The results indicate that the characteristics of the detector, road facilities, geometry, and other traffic environment factors affect errors in traffic volume detection.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.05a
• /
• pp.303-306
• /
• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKF uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.2B
• /
• pp.159-167
• /
• 2010

The purpose of this study is to get the adjusted radar rainfalls by ANN(Artificial Neural Network) method. In the case of radar rainfall, it has an advantage of spatial distribution characteristics of rainfall while point rainfall has an advantage at the point. Therefore we adjusted the radar rainfall by ANN method considering the advantages of two rainfalls of radar and point. This study constructed two ANN models of Model I and Model II for radar rainfall adjustment. We collected the three rainfall events and adjusted the radar rainfall for Anseong-cheon basin. The two events were inputted into the Modeland Model to derive the optimum parameters and the rest event was used for validation. The adjusted radar rainfalls by ANN method and the raw radar rainfall were used as the input data of ModClark model which is a semi-distributed model to simulate the runoff. As the results of the simulation, the runoff by raw radar rainfall were overestimated but the peak time and peak runoff from the adjusted rainfall by ANN were well fitted to the observed hydrograph.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2003.09a
• /
• pp.311-314
• /
• 2003

In radar tracking, since the sensor measures range, azimuth and elevation angle of a target, the measurement equation is nonlinear and the extended Kalman filter (EKF) is applied to nonlinear estimation. The conventional EKF has been widely used as a nonlinear filter for radar tracking, but the considerably large measurement error due to the linearization of nonlinear function in highly nonlinear situations may deteriorate the performance of the EKF. To solve this problem, a fuzzy-model-based Kalman filter (FMBKF) is proposed for radar tracking. The FMBKP uses a local model approximation based on a TS fuzzy model instead of a Jacobian matrix to linearize nonlinear measurement equation. The hybrid GA and RLS method is used to identify the premise and the consequent parameters and the rule numbers of this TS fuzzy model. In two-dimensional radar tracking problem, the proposed method is compared with the conventional EKF.

• Journal of electromagnetic engineering and science
• /
• v.17 no.2
• /
• pp.51-56
• /
• 2017

This study proposes a modeling process of equivalent terrains to reduce the computational load and time of a full-wave electromagnetic (EM) simulation. To verify the suitability of the proposed process, an original terrain model with a size of $3m{\times}3m$ is equivalently quantized based on the minimum range resolution of a radar, and the radar image of the quantized model is compared with that of the original model. The results confirm that the simulation time can be reduced from 407 hours to 162 hours without a significant distortion of the radar images, and an average estimation error of the quantized model (20.4 mm) is similar to that of the original model (20.3 mm).