• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.1
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• pp.14-19
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• 2010

In this paper, an efficient algorithm is developed to perform target rotational motion compensation to achieve the clear inverse synthetic aperture radar(ISAR) image. The algorithm is based on a time-frequency technique. This algorithm provides an efficient method to resolve the blurring image caused by the time-varying behavior of the target scattering centers and leads to a well-focused ISAR image. Results demonstrate that the time-frequency techniques can improve the blurring ISAR image when an aircraft is in complex motion, such as maneuvering, rotation and acceleration.

• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.4
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• pp.451-458
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• 2014

This paper introduces a bistatic ISAR imaging technique. In bistatic geometry, the transmitter and receiver are placed in different locations. The monostatic ISAR is inadequate not only for obtaining images on targets approaching along the radar's line of sight, but also for stealth targets. In this paper, geometry, signal modeling as well as bistatic Doppler for bistatic ISAR are introduced to address these problems. Simulations results show bistatic ISAR images as well as monostatic ISAR images against target's moving scenarios, and analyze their differences for each scenario.

• Journal of the Korea Institute of Military Science and Technology
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• v.23 no.2
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• pp.97-105
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• 2020

In order to set up radar cross section(RCS) reduction factors for a target, the scattering point position of the target should be identified through inverse synthetic aperture radar(ISAR) image analysis. For this purpose, ISAR image focusing is important. Maritime ship is non-linear maneuvering in the sea, however, which blur the ISAR image. To solve this problem, translational and rotational motion compensation are essential to form focused ISAR image. In this paper, hourglass and ISAR image analysis are performed on the collected data in the sea instead of using the prediction software tool, which takes much time and cost to make computer-aided design(CAD) model of the ship.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.2
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• pp.245-250
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• 2014

This paper introduces the inverse synthetic aperture radar(ISAR) imaging technique for rear view target of an automobile, which uses both linear frequency modulation-frequency shift keying(LFM-FSK) waveform and monopulse tracking. LFM-FSK waveform consists of two sequential stepped frequency waveforms with some frequency offset, and thus, can be used to generate ISAR images of rear view target of an automobile. However, ISAR images can often be blurred due to non-uniform change rate of relative aspect angle between radar and target. In order to address this problem, one-dimensional(1-D) Lagrange interpolation technique in conjunction with angle information obtained from the monopulse tracking is applied to generate uniform data across the radar's aspect angle. Simulation results show that the proposed method can provide focused ISAR images.

• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.8
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• pp.670-677
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• 2017

In this paper, we propose a rotational motion compensation(RMC) for bistatic inverse synthetic aperture radar(Bi-ISAR) imaging. For this purpose, geometry-error, caused by changes of bistatic-angle, is removed using known position information of a transmitter, a receiver, and target trajectories. Next, RMC is performed to compensate non-uniform rotational motion error by reformatting radar signal in terms of a newly defined slow time variable that converts non-uniform rotational motion into uniform one. Simulation results using an aircraft model composed of ideal point scatterers validate the efficacy of the proposed Bi-ISAR RMC method.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.9
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• pp.952-958
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• 2014

In this paper, we propose a parametric sparse recovery algorithm(SRA) applied to a radar signal model, based on the compressive sensing(CS), for the ISAR(Inverse Synthetic Aperture Radar) image reconstruction from an incomplete radar-cross-section(RCS) data and for the estimation of rotation rate of a target. As the SRA, the iteratively-reweighted-least-square(IRLS) is combined with the radar signal model including chirp components with unknown chirp rate in the cross-range direction. In addition, the particle swarm optimization(PSO) technique is considered for searching correct parameters related to the rotation rate. Therefore, the parametric SRA based on the IRLS can reconstruct ISAR image and estimate the rotation rate of a target efficiently, although there exists missing data in observed RCS data samples. The performance of the proposed method in terms of image entropy is also compared with that of the traditional interpolation methods for the incomplete RCS data.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.5
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• pp.566-573
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• 2013

Inverse synthetic aperture radar(ISAR) images have been widely used in non-cooperative target recognition(NCTR). One of the most important issues in ISAR imaging is the improvement of the image smeared by target motion. In this paper, we propose the discrete Gabor representation(DGR) in an oversampling scheme for efficient acquisition of high-quality ISAR images. The DGR compartmentally assigns the Gabor coefficients to unit cells of the time-frequency grid related to the given Gabor logons. Thus, it can show an excellent time-frequency concentration and effectively discriminates the Doppler components from point-scatterers. The simulation results demonstrated that the DGR not only obtained high-quality ISAR images but also retained computational efficiency.