• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.6
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• pp.564-571
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• 2015

In this paper, we propose a multiple-input, multiple-output(MIMO) interferometric radar network system to generate three-dimensional (3-D) MIMO interferometric inverse synthetic aperture radar(InISAR) image. In the MIMO interferometric radar network system, the MIMO InISAR image can be formed by an incoherent summation of multiple bistatic InISAR images that show 3-D scatterers of a target observed at different bistatic interfermetric configurations, respectively. Because bistatic-sccattering physics of a target at different viewpoints are visible in the 3-D MIMO InISAR image, it can provide various scatterering physics properties of a target, and can be used for target classification as a useful feature vector. Simulations validate that our proposed method successfully finds locations of scatterers of a target in MIMO radar interferometric network system.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.

• ETRI Journal
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• v.43 no.6
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• pp.991-1003
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• 2021

Automatic modulation classification is essential in radar emitter identification. We propose a cascade classifier by combining a support vector machine (SVM) and convolutional neural network (CNN), considering that noise might be taken as radar signals. First, the SVM distinguishes noise signals by the main ridge slice feature of signals. Second, the complex envelope features of the predicted radar signals are extracted and placed into a designed CNN, where a modulation classification task is performed. Simulation results show that the SVM-CNN can effectively distinguish radar signals from noise. The overall probability of successful recognition (PSR) of modulation is 98.52% at 20 dB and 82.27% at -2 dB with low computation costs. Furthermore, we found that the accuracy of intermediate frequency estimation significantly affects the PSR. This study shows the possibility of training a classifier using complex envelope features. What the proposed CNN has learned can be interpreted as an equivalent matched filter consisting of a series of small filters that can provide different responses determined by envelope features.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.351-375
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• 2018

This paper aims to i) introduce the characteristics of HF ocean radar and the major results and information produced by the radar networks in the Korean coasts to the readers, ii) make an up-to-date inventory of the existing radar systems, and iii) share the information related to the radar operating skill and the ocean current data application. The number of ocean radars has been showing a significant growth over the past 20 years, currently deploying more than 44 radars in the Korean coasts. Most of radars are in operation at the present time for the purposes related to the marine safety, tidal current forecast and understanding of ocean current dynamics, mainly depending on the mission of each organization operating radar network. We hope this overview paper may help expand the applicability of the ocean radar to fisheries, leisure activity on the sea, ocean resource management, oil spill response, coastal environment restoration, search and rescue, and vessel detection etc., beyond the level of understanding of tidal and ocean current dynamics. Additionally we hope this paper contributes further to the surveillance activity on our ocean territory by founding a national ocean radar network frame and to the domestic development of ocean radar system including signal processing technology.

• Journal of Advanced Navigation Technology
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• v.22 no.5
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• pp.429-435
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• 2018

In radar systems, FFT (fast Fourier transform) operation is necessary to obtain the range and velocity of target, and the design of an FFT processor which operates at high speed is required for real-time implementation. The perfect shuffle network is suitable for high-speed FFT processor. In particular, twice perfect shuffle network based on radix-4 is preferred for very high-speed FFT processor. Moreover, radar systems that requires various velocity resolution should support scalable FFT points. In this paper, we propose a 8~1024-point scalable FFT processor based on twice perfect shuffle network algorithm and present hardware design and implementation results. The proposed FFT processor was designed using hardware description language (HDL) and synthesized to gate-level circuits using $0.65{\mu}m$ CMOS process. It is confirmed that the proposed processor includes logic gates of 3,293K.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.7
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• pp.1858-1872
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• 2023

With the advance of radar technologies, the availability of synthetic aperture radar (SAR) images increases. To improve application of SAR images, a management system for SAR images is proposed in this paper. The system provides trainable land cover classification module and display of SAR images on the map. Users of the system can create their own classifier with their data, and obtain the classified results of newly captured SAR images by applying the classifier to the images. The classifier is based on convolutional neural network structure. Since there are differences among SAR images depending on capturing method and devices, a fixed classifier cannot cover all types of SAR land cover classification problems. Thus, it is adopted to create each user's classifier. In our experiments, it is shown that the module works well with two different SAR datasets. With this system, SAR data and land cover classification results are managed and easily displayed.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.11a
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• pp.66-69
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• 2017

본 논문에서는 깊은 합성 곱 신경망 (Deep Convolutional Neural Network) 를 이용해서 SAR (Synthetic Aperture Radar) 영상의 반전 잡음 (speckle noise) 성분을 제거하는 기법을 제안하고자 한다. Deep Convolutional Neural Network는 이미지의 데이터 특성에 적합한 딥 러닝 방법이고, 이는 SAR 위성영상의 반전 잡음 제거에 사용해도 효과적이다. 반전 잡음 필터 모델 추정을 위한 학습은 임의로 반전 잡음을 합성한 트레이닝 이미지들과 원본 트레이닝 이미지들을 이용한 회귀모델을 통해 진행된다. 학습을 통해 얻은 반전 잡음 필터는 기존 알고리즘에 비해 우수한 외곽선 보존 성능을 나타냄을 확인하였다.

• Geomechanics and Engineering
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• v.37 no.5
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• pp.467-474
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• 2024

Monitoring and managing the condition of underground utilities is crucial for ground stability. This study aims to determine whether images obtained using ground penetrating radar (GPR) accurately reflect the characteristics of buried pipelines through image analysis. The investigation focuses on pipelines made from different materials, namely concrete and steel, with concrete pipes tested under various diameters to assess detectability under differing conditions. A total of 400 images are acquired at locations with pipelines, and for comparison, an additional 100 data points are collected from areas without pipelines. The study employs GPR at frequencies of 200 MHz and 600 MHz, and image analysis is performed using machine learning-based convolutional neural network (CNN) techniques. The analysis results demonstrate high classification reliability based on the training data, especially in distinguishing between pipes of the same material but of different diameters. The findings suggest that the integration of GPR and CNN algorithms can offer satisfactory performance in exploring the ground's interior characteristics.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.6
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• pp.611-619
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• 2016

Success in modern war depends on electronic warfare. Therefore, It is very important to identify the kind of Radar PRI modulations in a lot of Radar electromagnetic waves. In this paper, I propose an algorithm to identify Linear up Sliding PRI, Non-Linear up Sliding PRI and Linear Down Sliding PRI, Non-Linear Down Sliding PRI among many Radar pulses. We applied not only the TDOA(Time Difference Of Arrival) concept of Radar pulse signals incoming to antennas but also a rising and falling curve characteristics of those PRI's. After making a program by such algorithm, we input each 40 data to those PRI's identification programs and as a result, those programs fully processed the data in according to expectations. In the future, those programs can be applied to the ESM, ELINT system.

• Journal of Navigation and Port Research
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• v.46 no.6
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• pp.509-516
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• 2022

Recently, ships have begun using the Asterix CAT 240 format as a method for transmitting radar image data to other devices. However, the Asterix format has a flexible structure that can be defined by the user, and a format structure defined as unsuitable for ship radar operation may undesirably increase navigational equipment network traffic or reduce stability. Therefore, to reduce the traffic of the navigation network and enhance the stability, a method of defining the optimized Asterix CAT 240 format with an appropriate setting value according to the performance of the radar scanner and display device was studied.