• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.1
• /
• pp.130-138
• /
• 2003

The classical image reconstruction for stripmap-mode Synthetic Aperture Radar is the Range-Doppler algorithm. When the spotlight-mode SAR system was envisioned, Range-Doppler algorithm turned out to fail rapidly in this SAR imaging modality. Thus, what is referred to as Polar format algorithm, which is based on the Plane wave approximation, was introduced for imaging from spotlight-mode SAR raw- data. In this paper, we have studied for the raw data processing schemes in the spotlight-mode Synthetic Aperture Radar. We apply the Wavefront Reconstruction scheme that does not utilize the approximation in spotlight-mode SAR imaging modelity, and compare the performance of target imaging with the Polar format inversion scheme.

• Current Industrial and Technological Trends in Aerospace
• /
• v.3 no.1
• /
• pp.65-71
• /
• 2005

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.19 no.10
• /
• pp.1175-1184
• /
• 2008

Delay-sum back projection(DSBP) algorithm and the time reversal algorithm based on the finite-difference time-domain method are compared. The two algorithms, which operate in the time domain, can process the ultra-wideband (UWB) radar data to generate images that are close to the original location and shape of the target. For the experiment, the UWB radar consists of a network analyzer, a resistive V dipole antenna, a scanner, and a control computer. The radar aperture is synthesized by linearly scanning the antenna. A calibration procedure is applied to the measured data to remove signal distortion and clutter. The two algorithms are applied to the same data on the same platform. It is shown that the DSBP algorithm produces better images but takes longer time to produce the images than the FDTD-TR algorithm.

• Bulletin of the Korean Space Science Society
• /
• 2011.04a
• /
• pp.25.1-25.1
• /
• 2011

다목적 실용위성 5호는 국내 최초로 합성 개구면 레이더(SAR)를 장착한 지구 관측위성으로서 2011년 중반에 러시아의 Dnepr 로켓에 의해 발사되어 평균 고도 550 km의 태양동기 여명궤도에서 운용될 예정이다. 위성은 28일을 주기로 지구를 421회 공전하는 반복 지상궤적을 가지며 인터페로메트리 레이더 영상의 획득을 위해 위성이 지구적도 상공을 통과할 때 기준경도로부터 ${\pm}2$ km 이내로 지상궤적이 유지될 수 있도록 궤도조정을 수행한다. 위성은 궤도에 투입된 후 2개월 이내에 정상적인 지상궤적을 획득하고 몽골에 설치된 레이더 반사판을 이용하여 4개월에 걸친 검보정을 수행한 후에 정상적인 운용에 들어가게 된다. 이 연구에서는 위성이 발사체와 분리된 이후 정상적인 지상궤적을 획득하는데 걸리는 시간을 분석하고 위성의 지상궤적을 기준 경도로부터 ${\pm}2$ km 이내로 유지시키기 위한 궤도조정에 필요한 조정주기와 연료소모량을 분석한다.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.47 no.8
• /
• pp.566-573
• /
• 2019

In order to obtain high-quality images by motion compensation in the airborne synthetic aperture radar (SAR), accurate motion sensing in image acquisition section is necessary. Especially, reducing relative position error and discontinuity in motion sensing is important. To overcome the problem, we propose a pre-parametric error modeling (P-PEM) algorithm which is a real-time motion sensing algorithm for the airborne SAR in this paper. P-PEM is an extended version of parametric error modeling (PEM) method which is a motion sensing algorithm to mitigate the errors in the previous work. PEM estimates polynomial coefficients of INS error which can be assumed as a polynomial in the short term. Otherwise, P-PEM estimates polynomial coefficients in advance and uses at image acquisition section. Simulation results show that the P-PEM reduces relative position error and discontinuity effectively in real-time.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.2
• /
• pp.147-155
• /
• 2024

Based on the recently developed deep learning technology, many studies have been conducted on deep learning networks that simultaneously detect and classify targets of interest in synthetic aperture radar(SAR) images. Although numerous research results have been derived mainly with the open SAR ship datasets, there is a lack of work carried out on the deep learning network aimed at detecting and classifying SAR ground targets and trained with the synthetic dataset generated from electromagnetic scattering simulations. In this respect, this paper presents the deep learning network trained with the synthetic dataset and applies it to detecting and classifying real SAR ground targets. With experiment results, this paper also analyzes the network performance according to the composition ratio between the real measured data and the synthetic data involved in network training. Finally, the summary and limitations are discussed to give information on the future research direction.

• Aerospace Engineering and Technology
• /
• v.10 no.2
• /
• pp.37-48
• /
• 2011

Synthetic Aperture Radar(SAR) is a powerful and well established microwave remote sensing technique which enables high resolution measurement of Earth surface independent of weather conditions and sunlight illumination. KARI has been developing the first Korea SAR satellite which is scheduled to be launched in this year. The SAR satellite mainly consists of the bus platform and SAR payload. Most of all, the RF compatible design during the design phase and the verification of the RF compatibility during the testing phase is very important procedure for the in-orbit performance guarantee because the SAR payload radiates high power through the SAR antenna. In this study, the SAR satellite design criteria and verification procedure for the RF compatibility are described. In addition, this paper describes the RF full radiation testing (RF auto-compatibility testing) for the verification of the RF performance robustness, the testing configuration, and the test results.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.24 no.5
• /
• pp.549-557
• /
• 2013

This paper describes a block processing approach to detect targets in front of mono-static terrain imaging radar (TIR). It is difficult to employ several conventional imaging methods of the synthetic aperture radar(SAR) because the TIR is an ultra-wide-band(UWB) type of radar and employs a dechirp-on-receive process. To design an available imaging method, a block processing approach which conducts a range compression and an azimuth compression is proposed in this paper. The complete derivation of the proposed approach is presented. The results of simulations and field tests are demonstrated to show the performance and validity of the proposed approach.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.22 no.6
• /
• pp.735-744
• /
• 2019

One of the key factors for recognition performance in the automatic target recognition for synthetic aperture radar imagery(SAR-ATR) system is reliability of the SAR target database. To achieve optimal performance, the database should be constructed using the images obtained under the same operating condition as the SAR sensor. However, it is impractical to have the extensive set of real-world SAR images, and thus those from the electro magnetic prediction tool with 3-D CAD models are suggested as an alternative where their reliability can be always questionable. In this paper, a method for similarity assessment between target SAR images is presented inspired by the fact that a target SAR image is mainly characterized by the features of scattering centers. The method is demonstrated using a variety of examples and quantitatively measures the similarity related to reliability. Its assessment performance is further compared with that of the existing metric, structural similarity(SSIM).

• Journal of the Korea Institute of Military Science and Technology
• /
• v.25 no.3
• /
• pp.219-230
• /
• 2022

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.