• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.2
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• pp.207-213
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• 2013

This study describes an omega-K algorithm for focusing bistatic synthetic aperture radar(SAR) data using bistatic-to-monostatic conversion. Bistatic SAR system considered in this study consists of a transmitting antenna and a physical array of several receiving antennas. The length of the physical array is identical to the SAR synthetic aperture. Unlike the monostatic case, an omega-K algorithm for the bistatic case is difficult to obtain the exact equation in the 2D wavenumber domain. The key of the proposed algorithm is converting the bistatic data into a monostatic one. The effectiveness of the proposed algorithm is proved by simulation and real measurement data.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.3
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• pp.475-482
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• 2023

Synthetic Aperture Radar (SAR), which provides images of targets using radio signals, enables monitoring at all times regardless of weather conditions. In this paper, the SAR system was installed on the test aircraft to collect SAR raw data on the ground and the sea, and the results of image formation using the backprojection algorithm were presented.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.2
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• pp.189-195
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• 2015

There are many research works on the SAR image generation using EM(Electro Magnetic) simulation. Particularly, there are several dedicated S/Ws for SAR image generation and analysis. But, most of them are not available to the public due to the reason for defense and security. In this paper, we describe the generation of ISAR images for a realistic target model using the general purpose EM simulator like FEKO. This method can benefit us many advantages like building the database of many targets for target recognition with cost-and-time effective way.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.40 no.1
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• pp.49-60
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• 2012

Synthetic aperture radar (SAR) has been employed in various fields due to its capability to generate high resolution images regardless of weather and visibility. This paper presents a performance analysis on the image formation of high resolution SAR according to various slant range distance and synthetic aperture lengths using a range migration algorithm simulator. Although the visual performance on the SAR image is more accurate, a numeric analysis resulted in a comparable measurement. More specifically, raw data were generated for an ideal point target upon imaging geometries and design parameters such as slant range distance and synthetic aperture lengths. Finally, spatial resolution, peak to sidelobe ratio and integrated sidelobe ratio are drawn to provide SAR capabilities in the initial concept design, final in-flight calibration and validation stages.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1957-1964
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• 1994

In this thesis, a Synthetic Aperture Rarar Processor that is possible real-time handling is implemented using CW(Continuose Wave) laser as a light source, CCD(charge Coupled Device) as a time integrator, and AOD(Acousto-Optic Device) as the space integrator. One of the advantages of the proposed system is that it does not require driving circuits of the light source. To implement the system, the linear frequency modulation(chirp) technique has been used for radar signal. The received data for the unit target was processed using 7.80 board and accompanying electronic circuits. In order to reduce the smear effect of the focused chirp signal which occurs Bragg diffrection angle of the AOD has been utilized to make sharp pulses of the laser source, and the pulse made synchronized with the chirp signal. Experiment and analysis results of the data and images detected from CCD of the proposed SAR system demonstrated that detection effect is degrated as the unit target distance increases, and the resolving power is improved as the bandwidth of the chirp signal increases. Also, as the pulse width of the light source decreases, the smear effect has been reduced. The experimental results assured that the proposed system in this papre can be used as a real time SAR processor.

• Journal of the Korea Society for Simulation
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• v.29 no.4
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• pp.21-31
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• 2020

NCTR(Non-Cooperative Target Recognition) refers to the function of radar to identify target on its own without support from other systems such as ELINT(ELectronic INTelligence). ISAR(Inverse Synthetic Aperture Radar) image is one of the representative methods of NCTR, but it is difficult to automatically classify the target without an identification database due to the significant changes in the image depending on the target's maneuver and location. In this study, we discuss how to build an identification database using simulation and deep-learning technique even when actual images are insufficient. To simulate ISAR images changing with various radar operating environment, A model that generates and learns images through the process named 'Perfect scattering image,' 'Lost scattering image' and 'JEM noise added image' is proposed. And the learning outcomes of this model show that not only simulation images of similar shapes but also actual ISAR images that were first entered can be classified.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.8
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• pp.636-645
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• 2017

A scanning radar is exploited widely such as for ground surveillance, disaster rescue, and etc. However, the range resolution is limited by transmitted bandwidth and cross-range resolution is limited by beam width. In this paper, we propose a method for super-resolution radar imaging. If the distribution of reflectivity is sparse, the distribution is called sparse signal. That is, the problem could be formulated as compressive sensing problem. In this paper, 2D super-resolution radar image is generated via reweighted ${\ell}_1-Minimization$. In the simulation results, we compared the images obtained by the proposed method with those of the conventional Orthogonal Matching Pursuit(OMP) and Synthetic Aperture Radar(SAR).

• Journal of the Korea Institute of Military Science and Technology
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• v.7 no.2 s.17
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• pp.88-99
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• 2004

A development of a software on radar target signature analysis is presented in this paper The target signature includes Radar Cross Section(RCS) prediction, Range Profile(RP) processing and Inverse Synthetic Aperture Radar(ISAR) processing. Physical Optics(PO) is the basic calculation method for RCS prediction and Geometrical Optics(GO) is used for ray tracing in the field calculation of multiple reflection. For RP and ISAR, Fast Fourier Transform(FFT) and Matrix Pencil(MP) method were implemented for post-processing. Those results are integrated into two separate softwares named as Radar Target Signature Generator(RTSG) and Radar Target Signature Analyser(RTSA). Several test results show good performances in radar signature prediction and analysis.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.655-662
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• 2011

SAR(Synthetic Aperture Radar) has the ability to generate high resolution images regardless of a weather condition(e.g. visibility good or poor and day or night, etc.). SAR is considered as one of the most important powers and needs for the future since it has been utilized in a number of important military fields such as early warning, urban defense, missile guidance system, etc. Additionally there are many civilian demands and applications in aviation, traffic control, earth and space explorations, weather forecast etc. This days, the ability to acquire and analyze information is needed to cope with the urgency of global politics and international changes. In this paper, technical survey and development review for SAR systems are investigated to derive the core and immature technologies of domestic defense industry.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.2-2
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• 2023

기후변화가 가속화로 인해 수재해의 빈도와 강도 예측이 어려워짐에 따라 실시간 홍수 모니터링에 대한 수요가 증가하고 있다. 합성개구레이다는 광원과 날씨에 무관하게 촬영이 가능하여 수재해 발생시에도 영상을 확보할 수 있다. 합성개구레이다를 활용한 수체 탐지 알고리즘 개발이 활발히 연구되어 왔고, 딥러닝의 발달로 CNN을 활용하여 높은 정확도로 수체 탐지가 기능해졌다. 하지만, CNN 기반 수체 탐지 모델은 훈련시 높은 정량적 정확성 지표를 달성하여도 추론 후 정성적 평가시 경계와 소하천에 대한 탐지 정확성이 떨어진다. 홍수 모니터링에서 특히 중요한 정보인 경계와 좁은 하천에 대해서 정확성이 떨어짐에 따라 실생활 적용이 어렵다. 이에 경계를 강화한 적대적 학습 기반의 수체 탐지 모델을 개발하여 더 세밀하고 정확하게 탐지하고자 한다. 적대적 학습은 생성적 적대 신경망(GAN)의 두 개의 모델인 생성자와 판별자가 서로 관여하며 더 높은 정확도를 달성할 수 있도록 학습이다. 이러한 적대적 학습 개념을 수체 탐지 모델에 처음으로 도입하여, 생성자는 실제 라벨 데이터와 유사하게 수체 경계와 소하천까지 탐지하고자 학습한다. 반면 판별자는 경계 거리 변환 맵과 합성개구레이다 영상을 기반으로 라벨데이터와 수체 탐지 결과를 구분한다. 경계가 강화될 수 있도록, 면적과 경계를 모두 고려할 수 있는 손실함수 조합을 구성하였다. 제안 모델이 경계와 소하천을 정확히 탐지하는지 판단하기 위해, 정량적 지표로 F1-score를 사용하였으며, 육안 판독을 통해 정성적 평가도 진행하였다. 기존 U-Net 모델이 탐지하지 못하던 영역에 대해 제안한 경계 강화 적대적 수체 탐지 모델이 수체의 세밀한 부분까지 탐지할 수 있음을 증명하였다.