• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.27 no.1
• /
• pp.69-75
• /
• 2016

In this paper, we propose a inverse synthetic aperture radar(ISAR) rotational motion compensation(RMC) method to remove residual blurring caused by non-uniform rotational motion of a target. First, a range bin having an isolated scatterer is selected. Next, polynomial phase signal in the selected range bin is estimated by using both Fourier transform(FT) and polynomial-phase transform(PPT). Finally, a new slow time variable that uniformly samples radar signal along the aspect angle directions is defined by using the estimated phase signal, and we interpolate radar signal in terms of the new time variable. As a result, rotational motion to blurr ISAR images is removed, and focused ISAR images are obtained. Simulation results using battleship model validate the robustness and effectiveness of our proposed RMC method.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.45 no.2
• /
• pp.12-19
• /
• 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
• /
• v.25 no.4
• /
• pp.451-458
• /
• 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 Korean Society for Aeronautical & Space Sciences
• /
• v.43 no.1
• /
• pp.40-48
• /
• 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
• /
• v.25 no.9
• /
• pp.952-958
• /
• 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
• /
• v.28 no.8
• /
• pp.670-677
• /
• 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.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.17 no.1
• /
• pp.67-76
• /
• 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
• /
• v.26 no.2
• /
• pp.218-225
• /
• 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.

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

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.550-553
• /
• 2005

To acquire radar target signature, various kinds of target are necessary. Measurement is one of the data acquiring method, but much time and high cost is required to get the target data from the real targets. Even if we can afford that, the targets we can access are very limited. To obtain target signatures avoiding these problems, we build the target CAD (Computer Aided Design) model for the calculation of target signatures. To speed up RCS calculation, we applied adaptive super-sampling and tested quite complex tank CAD model which is 1.4 hundred of thousands facet. We use calculated RCS data for ID range profile and 2D ISAR (Inverse Synthetic Aperture Radar) image formation. We adopted IFFT (Inverse Fast Fourier Transform) algorithm combined with polar formatting algorithm for the ISAR imaging. We could confirm the possibility of the construction of database from the images of CAD models for target classification applications.