• 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.

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

• Journal of the Korea Institute of Military Science and Technology
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• v.24 no.2
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• pp.175-186
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• 2021

In the field of automatic target recognition(ATR) with synthetic aperture radar(SAR) imagery, it is usually impractical to obtain SAR target images covering a full range of aspect views. When the database consists of SAR target images with limited angular diversity, it can lead to performance degradation of the SAR-ATR system. To address this problem, this paper proposes a deep learning-based method where channel attention modules(CAMs) are inserted to a convolutional neural network(CNN). Motivated by the idea of the squeeze-and-excitation(SE) network, the CAM is considered to help improve recognition performance by selectively emphasizing discriminative features and suppressing ones with less information. After testing various CAM types included in the ResNet18-type base network, the SE CAM and its modified forms are applied to SAR target recognition using MSTAR dataset with different reduction ratios in order to validate recognition performance improvement under the limited angular diversity condition.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.26 no.2
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• pp.218-225
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• 2015

Inverse synthetic aperture radar(ISAR) is coherent imaging system formed by conducting signal processing of received data which consists of radar cross section(RCS) reflected from maneuvering target. A novel algorithm is proposed to compensate inter-pulse motion(IPM) for the purpose of forming an well-focused ISAR image through signals generated by stepped chirp waveform( SCW). The velocity and acceleration of the target related to IPM are estimated based on particle swarm optimization (PSO) which has been widely used in optimization technique. Furthermore, a modified PSO which enables us to improve the performance of PSO is used to compensate IPM in a very short-time. Simulation results using point scatterer model of a Boeing-737 aircraft validate the performance of the proposed algorithm.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.7
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• pp.588-595
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• 2013

Airborne SAR system is the imaging Radar system that is loaded on a manned or unmanned aircraft, which is in charge of high quality image acquisition and moving target detection. This paper describes the operational requirements for the Airborne SAR system and suggests the operational concept to satisfy the requirements. To be specific, it describes the interface with airborne system, state definition and transition, operation mode based on mission definition file, fault management, and data storing and transmission concept. Finally, it gives the ground test results to verify the SAR system operational concept.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.1
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• pp.30-44
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• 2022

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

• Current Industrial and Technological Trends in Aerospace
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• v.8 no.1
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• pp.45-54
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• 2010

The goal of the paper is to discuss the recent trend of the configuration of high resolution LEO(Low Earth Orbit) EO(Earth Observation) satellites. The satellite configuration is decided by considering several factors such as mission, payloads, launch vehicle, propulsion and attitude control module. The advent of commercial companies selling satellite's images in 2000's requires additional changes of the satellite system to be capable of obtaining many high resolution images quickly. In order to meet customer's needs, the overall configuration of satellites is designed to be compact and stable without the loss of structural integrity and reliability. Among design changes, the configuration change of satellites is treated intensively in the paper.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.1
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• pp.40-48
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• 2015

In this paper, we suggest a measurement modeling of the RF seeker using the ISAR(Inverse Synthetic Aperture Radar) image. Reference scattering points are extracted first from ISAR images which are changed according to target attitude. And then uncertainties included in RF seeker measurement such as noise strength, blink, and boresight error are added to the reference scattering points. The proposed measurement model of the RF seeker can be used to develop various kinds of target tracking algorithms.

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