• Current Industrial and Technological Trends in Aerospace
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• v.7 no.2
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• pp.25-32
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• 2009

In this paper, the technical trends of the synthetic aperture radar (SAR) were studied by investigating the journals and conference associated with the advanced technology of SAR. SAR has been first demonstrated in 1950. The main objective of SAR development is to overcome the limitations of real aperture radars. From 1950, many new concepts and technologies for SAR system is suggested and realized by many international researchers and engineers. New concepts for future SAR systems represented in the recent conference have been rearranged and analyzed.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.5
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• pp.443-461
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• 2022

Target Detection in synthetic aperture radar(SAR) image is critical for military and national defense. In this paper, we propose YOLOv4-Attention architecture which adds attention modules to YOLOv4 backbone architecture to complement the feature extraction ability for SAR target detection with high accuracy. For training and testing our framework, we present new SAR embedding datasets based on MSTAR SAR public datasets which are about poor environments for target detection such as various clutter, crowded objects, various object size, close to buildings, and weakness of signal-to-clutter ratio. Experiments show that our Attention YOLOv4 architecture outperforms original YOLOv4 architecture in SAR image target detection tasks in poor environments for target detection.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.105-113
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• 2010

SAR(Synthetic Aperture Radar) has characteristics well-suited for the measurement of geophysical parameters during day and night in all weather conditions. Recently, SAR data with high resolution acquired by satellites became available to the public. In such data, many features and phenomena of geometric structure of man-made objects and natural environments become observable. In this paper, we discuss main considerations including geometric distortion and coregistration for efficient utilization of high resolution SAR images. And, various advanced technologies in SAR application fields are introduced.

• Journal of Astronomy and Space Sciences
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• v.40 no.4
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• pp.225-235
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• 2023

This paper presents the structural design of a planar synthetic aperture radar (SAR) antenna applied to a microsatellite. For micro-satellite applications, the SAR antenna structure must be lightweight, flat, and designed to withstand the launch environment. To satisfy these conditions, our novel antenna structure was designed using aluminium (AL) alloy. Structural analysis was performed for quasi-static load, random vibration, and shock load to verify its robustness in the launch environment, and the results are presented here.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.99-101
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• 2017

최근 딥러닝을 이용한 SAR 영상의 목표물을 인식하는 알고리즘이 괄목할만한 성능을 보여주었다. 이러한 알고리즘들은 포즈 각도 정보를 무시한 채 목표물의 종류를 추정하는 것에만 초점을 맞춘다. 포즈 각도 추정 알고리즘은 단지 SAR 영상 목표물 인식 알고리즘의 전처리 과정으로 연구되었다. 하지만 감시 시스템에서, 목표물이 향하고 있는 방향을 추정하는 것 또한 중요하다. 먼저, 포즈 각도 추정을 통하여 적의 전술 배치를 계획을 추정할 수 있다. 또한 목표물이 아군 쪽을 바라보면 큰 위협이 되는데, 포즈 각도 추정을 통하여 이러한 정보를 알 수 있다. 따라서 본 논문은 목표물이 향하고 방향을 추정할 수 있는 콘볼루션 네트워크를 고안하였다. 네트워크를 학습시키기 위하여 SAR 영상의 목표물의 포즈 각도를 양자화하여 포즈 각도 label 을 구성하였다. 또한 이러한 포즈 각도 추정을 정제하는 알고리즘을 고안하였고 이는 보다 정확한 포즈 각도 추정을 가능하게 하였다. 그 결과, 제안된 네트워크는 포즈 각도 추정에 높은 정확도를 보여준다.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.1433-1435
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• 2003

Very decisive progress was made in advancing fundamental POL-IN-SAR theory and algorithm development during the past decade. This was accomplished with the aid of airborne & shuttle platforms supporting single -to-multi-band multi-modal POL-SAR and also some POL-IN-SAR sensor systems, which will be compared and assessed with the aim of establishing the hitherto not completed but required missions such as tomographic and holographic imaging. Because the operation of airborne test-beds is extremely expensive, aircraft platforms are not suited for routine monitoring missions which is better accomplished with the use drones or UAVs. Such unmanned aerial vehicles were developed for defense applications, however lacking the sophistic ation of implementing advanced forefront POL-IN-SAR technology. This shortcoming will be thoroughly scrutinized resulting in the finding that we do now need to develop most rapidly POL-IN-SAR drone-platform technology especially for environmental stress-change monitoring with a great variance of applications beginning with flood, bush/forest-fire to tectonic-stress (earth-quake to volcanic eruptions) for real-short-time hazard mitigation. However, for routine global monitoring purposes of the terrestrial covers neither airborne sensor implementation - aircraft and/or drones - are sufficient; and there -fore multi-modal and multi-band space-borne POL-IN-SAR space-shuttle and satellite sensor technology needs to be further advanced at a much more rapid phase. The existing ENVISAT with the forthcoming ALOSPALSAR, RADARSAT-2, and the TERRASAT will be compared, demonstrating that at this phase of development the fully polarimetric and polarimetric-interferometric modes of operation must be viewed and treated as preliminary algorithm verification support modes and at this phase of development are still not to be viewed as routine modes.

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

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.102-104
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• 2017

최근 Convolutional neural network (CNN)을 도입하여, SAR 영상의 목표물 인식 알고리즘이 높은 성능을 보여주었다. SAR 영상은 4 종류의 polarization 정보로 구성되어있다. 기계와 신호처리의 비용으로 인하여 일부 데이터는 적은 수의 polarization 정보를 가지고 있다. 따라서 우리는 SAR 영상 data 를 멀티모달 데이터로 해석하였다. 그리고 우리는 이러한 멀티모달 데이터에 잘 작동할 수 있는 콘볼루션 신경망을 제안하였다. 우리는 데이터가 포함하는 모달의 수에 반비례 하도록 scale factor 구성하고 이를 입력 크기조절에 사용하였다. 입력의 크기를 조절하여, 네트워크는 특징맵의 크기를 모달의 수와 상관없이 일정하게 유지할 수 있었다. 또한 제안하는 입력 크기조절 방법은 네트워크의 dead filter 의 수를 감소 시켰고, 이는 네트워크가 자신의 capacity 를 잘 활용한다는 것을 의미한다. 또 제안된 네트워크는 특징맵을 구성할 때 다양한 모달을 활용하였고, 이는 네트워크가 모달간의 상관관계를 학습했다는 것을 의미한다. 그 결과, 제안된 네트워크의 성능은 입력 크기조절이 없는 일반적인 네트워크보다 높은 성능을 보여주었다. 또한 우리는 전이학습의 개념을 이용하여 네트워크를 모달의 수가 많은 데이터부터 차례대로 학습시켰다. 전이학습을 통하여 네트워크가 학습되었을 때, 제안된 네트워크는 특정 모달의 조합 경우만을 위해 학습된 네트워크보다 높은 성능을 보여준다.

• Journal of Space Technology and Applications
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• v.1 no.1
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• pp.33-40
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• 2021

Based on the requirements of a total weight of 42 kg or less, the NEXTSat-2 SAR (synthetic aperture radar) system was developed. As the NEXTSat-2 is a small-sized satellite, the SAR system was designed to account for about 40% of the dry mass of the payload relative to the total mass. Among the major components of the SAR system - which are an antenna, an RF transceiver, a baseband signal processor, and a power unit - a part with a particularly large dry mass is the antenna, the core of the SAR system. Whereas various selections are possible in consideration of gain and efficiency when designing the antenna, the micro-strip patch array antenna was adopted by reflecting the dry mass, power, and resolution required by the NEXTSat-2 project. In order to meet the mission requirement of the NEXTSat-2, the antenna was developed with a frequency of 9.65 GHz, a gain of 42.7 dBi, and a return loss of -15 dB. The performance of the antenna was verified by conducting a field test onboard the vehicle.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.487-495
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• 2022

This paper describes the design and development of a high-power transmit receive module(TRM) for mounting on X-band synthetic aperture radar(SAR) of the NEXTSat-2. The TRM generates a high-power pulse signal with a bandwidth of 100 MHz in the target frequency range of X-band and amplifies a low-noise on the received signal. Tx. path of the TRM has output signal level of more than 200 watts (53.01 dB), pulse droop of 0.35 dB, signal strength change of 0.04 dB during transmission signal output, and phase change of 1.7 ˚. Rx. path has noise figure of 3.99 dB and gain of 37.38 ~ 37.46 dB. It was confirmed the TRM satisfies all requirements. The TRM mounted on the NEXTSat-2 flight model(FM) which will be launched using the KSLV-II (Nuri).