• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.93-100
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• 2018

Inverse Synthetic Aperture Radar(ISAR) image is 2-dimensional radar cross section distributions of a target. For target approaching along radar's line of sight(LOS), the bistatic ISAR can compensate for the weakness of the monostatic ISAR which can not obtain the vertical resolution of the image. However, bistatic ISAR have longer processing times and variability in scattering mechanisms than monostatic ISAR, so target identification using only bistatic ISAR images can be inefficient. Therefore, this paper analyzes target identification performance using monostatic and bistatic ISAR images of targets approaching along radar's LOS and proposes a method of target identification through fusion of two radars. Simulation results demonstrate that identification performance through fusion is more efficient than identification performance using only monostatic, bistatic ISAR images.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.67-76
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• 2022

Monostatic/Bistatic inverse synthetic aperture radar (ISAR) images are two-dimensional radar cross section (RCS) distributions of a target. When there are many targets in a single radar beam, ISAR images are generated with targets overlapped, so it is difficult to perform the targets identification using the trained database. In addition, it is inefficient to perform target identification using only single monostatic and bistatic ISAR images separately because each method has its own advantages and weaknesses. Therefore, this paper analyzes multiple targets identification performances using monostatic/bistatic ISAR images and proposes a method of identification through fusion of two ISAR images. To identify multiple targets, we use image combination technique using trained single target images. Simulation results show effectiveness of proposed method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.6
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• pp.566-576
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• 2016

Inverse synthetic aperture radar(ISAR) image generated from bistatic radar(Bi-ISAR) represents two-dimensional scattering distribution of a target, and the Bi-ISAR can be used for bistatic target identification. However, Bi-ISAR has large variability in scattering mechanisms depending on bistatic configurations and do not represent exact range-Doppler information of a target due to inherent distortion. Thus, an efficient training DB construction is the most important factor in target identification using Bi-ISARs. Recently, a database construction method based on realistic flight scenarios of a target, which provides a reliable identification performance for the monostatic target identification, was applied to target identification using high resolution range profiles(HRRPs) generated from bistatic radar(Bi-HRRPs), to construct efficient training DB under bistatic configurations. Consequently, high identification performance was achieved using only small amount of training Bi-HRRPs, when the target is a considerable distance away from the bistatic radar. Thus, flight scenarios based training DB construction is applied to target identification using Bi-ISARs. Then, the capability and efficiency of the method is analyzed.

• Proceedings of the Korea Electromagnetic Engineering Society Conference
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• 2003.11a
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• pp.193-197
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• 2003

Radar target identification can be achieved by using various radar signatures, such as one-dimensional(1-D) range profile, 2-D radar images, and 1-D or 2-D scattering centers on a target. In this letter, five 1-D scattering center extraction methods are discussed - TLS(Total Least Square)-Prony, Fast Root-MUSIC (Multiple Signal Classification), Matrix-Pencil, GEESE(GEneralized Eigenvalues utilizing Signal-subspace Eigenvalues), TLS-ESPRIT(Total Least Squares - Estimation of Signal Parameters via Rotational Invariance Technique), These methods are compared in the context of estimation accuracy as well as a computational efficiency using a noisy data. Finally these methods are applied to the target classification experiment with the measured data in the POSTECH compact range facility.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.2
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• pp.198-207
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• 2016

Bistatric radar can perform detection and identification for stealth targets that are rarely detected by the conventional monostatic radar. However, high resolution range profile(HRRP) generated from the received signal in the bistatic radar cannot show exact range information of the target because the bistatic geometry lead to the distortions of the bistatic HRRP. In addition, electromagnetic scattering mechanisms of the target are varied depending on the bistatic geometry. Thus, efficient database construction is a crucial factor to achieve successful classification capability in bistatic target identification. In this paper, a database construction method based on realistic flight scenarios of a target, which provides a reliable identification performance for the monostatic radar, is applied to bistatic target identification. Then, the capability and efficiency of the method is analyzed. Simulation results show that reliable identification performance can be achieved using the database construction based on the flight scenarios when the target is a considerable distance away from the bistatic radar.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.2
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• pp.159-167
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• 2020

In actual battlefield environment, IFF radar plays an important role in distinguishing friend or foe targets and assigning unique identification code to management. Performance of IFF radar is greatly affected by radio environment including atmosphere and terrain, target maneuvering and operation mode. In this paper, M&S tool is consisted of interrogator(IFF radar) and answering machine(target) for radar performance analysis. The wave propagation model using APM(Advanced Propagation Model) and radar actuator system were modeled by considering beam waveform of individual operation beam mode. Using this tool, IFF radar performance was analyzed through two experimental results. As a result, it is expected that performance of IFF radar can be predicted in the operational environment by considering target maneuvering and operation beam mode.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.3
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• pp.289-295
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• 2009

HRR(High Resolution Range) profiles show one-dimensional radar images including electromagnetic scattering phenomena of a target. Thus, they are not only robust to noise, but also easily obtainable in a real-time. However, in order to construct a training database for the success of radar target identification, a huge amount of HRR profiles are needed because HRR profiles are highly dependent on the relative angle between the radar and the target. In order to alleviate this difficulty, a database construction method based on the scenarios of target's movement is proposed. The proposed method is able to provide a reliable target identification performance even with a small amount of training database.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.30 no.2
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• pp.152-159
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• 2019

In general, a Doppler radar can measure only the velocity of a moving target. To measure the distance of a moving target, it is necessary to use a frequency-modulated continuous wave or pulse radar. However, the latter are very complex in terms of both hardware as well as signal processing. Moreover, the requirement of wide bandwidth necessitates the use of millimeter-wave frequency bands of 24 GHz and 77 GHz. Recently, a new kind of Doppler radar using multitone frequency has been studied to sense the distance of moving targets in addition to their speed. In this study, we show that distance sensing of moving targets is possible by adjusting only the frequency of a 2.4 GHz Doppler radar with low cost phase lock loop. In particular, we show that distance can be sensed using only alternating current information without direct current offset information. The proposed technology satisfies the Korean local standard for low power radio equipment for moving target identification in the 2.4 GHz frequency band, and enables multiple long-range sensing and radio-frequency identification applications.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.2
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• pp.12-19
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• 2022

Artificial intelligence is driving the Fourth Industrial Revolution and is in the spotlight as a general-purpose technology. As the data collection from the battlefield increases rapidly, the need to us artificial intelligence is increasing in the military, but it is still in its early stages. In order to identify maritime targets, Republic of Korea navy acquires images by ISAR(Inverse Synthetic Aperture Radar) of maritime patrol aircraft, and humans make out them. The radar image is displayed by synthesizing signals reflected from the target after radiating radar waves. In addition, day/night and all-weather observations are possible. In this study, an artificial intelligence is used to identify maritime targets based on radar images. Data of radar images of 24 maritime targets in Republic of Korea and North Korea acquired by ISAR were pre-processed, and an artificial intelligence algorithm(ResNet-50) was applied. The accuracy of maritime targets identification showed about 99%. Out of the 81 warship types, 75 types took less than 5 seconds, and 6 types took 15 to 163 seconds.

• Korean Journal of Remote Sensing
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• v.40 no.2
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• pp.167-177
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• 2024

With advancements in satellite technology, interest in target detection and identification is increasing quantitatively and qualitatively. Synthetic Aperture Radar(SAR) images, which can be acquired regardless of weather conditions, have been applied to various areas combined with machine learning based detection algorithms. However, conventional studies primarily focused on the detection of stationary targets. In this study, we proposed a method to identify moving targets using an algorithm that integrates sub-aperture SAR images and cosine similarity calculations. Utilizing a transformer-based deep learning target detection model, we extracted the bounding box of each target, designated the area as a region of interest (ROI), estimated the similarity between sub-aperture SAR images, and determined movement based on a predefined similarity threshold. Through the proposed algorithm, the quantitative evaluation of target identification capability enhanced its accuracy compared to when training with the targets with two different classes. It signified the effectiveness of our approach in maintaining accuracy while reliably discerning whether a target is in motion.