• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.10a
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• pp.628-631
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• 2008

For the remote sensing purpose, radar systems extract the target information, such as the magnitude of reflectivity and the velocity from the spectrum analysis of return echoes through the Doppler filter bank. This conventional spectrum estimation method, FFT(Fast fourier Transform) is widely used in most radar systems. However, the frequency resolution of return echoes can be seriously degraded in fast moving targets because of the short acquisition time. Since the high Doppler frequency resolution is important in the detection and tracking of fast moving targets, it can cause very unsatisfactory results. Therefore, in this paper, the parameter spectrum estimation method called AR(Autoregressive) spectrum estimation, is investigated to overcome these problems.

• Journal of electromagnetic engineering and science
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• v.13 no.2
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• pp.73-85
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• 2013

In this paper we present an alternative approach to addressing the problem of designing cloaks for radar targets, which have been dealt with in the past by using the transformation optics (TO) algorithm. The present design utilizes realistic materials, which can be fabricated in the laboratory, and are wideband as well as relatively insensitive to polarization and incident angle of the incoming wave. The design strategy, presented herein, circumvents the need to use metamaterials for cloak designs that are inherently narrowband, dispersive and highly sensitive to polarization and incident angle. A new interpretation of the TO algorithm is presented and is employed for the design of radar cross section-reducing absorbers for arbitrary targets, and not just for canonical shapes, e.g., cylinders. The topic of performance enhancement of the absorbers by using graphene materials and embedded frequency structure surfaces is briefly mentioned. The design procedure for planar absorbing covers is presented and their performance as wrappers of general objects is discussed. A number of test cases are included as examples to illustrate the application of the proposed design methodology, which is a modification of the classical TO paradigm.

• Progress in Superconductivity and Cryogenics
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• v.23 no.4
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• pp.56-60
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• 2021

The microwave Radar used for special purposes in the past is being applied in various areas due to the technological advancement and cost reduction, and is particularly applied to autonomous driving in the automobile field. The FMCW (Frequency Modulated Continuous Wave) Radar can acquire level information of liquid in vessel based on the beat frequency obtained by continuously transmitting and receiving signals by modulating the frequency over time. However, for cryogenic fluids with small impedance differences between liquid medium and gas medium, such as liquid nitrogen and liquid hydrogen, it is difficult to apply a typical Radar-based level meter. In this study, we develop an 80 GHz FMCW Radar for level measurement of cryogenic fluids with small impedance differences between media and analyze its characteristics. Here, because of the low intrinsic impedance difference, most of the transmitted signal passes through the liquid nitrogen interface and is reflected at the bottom of the vessel. To solve this problem, a radar measurement algorithm was designed to detect multiple targets and separate the distance signal to the bottom of the vessel in order to estimate the precise position on the liquid nitrogen interface. Thereafter, performance verification experiments were performed according to the liquid nitrogen level using the developed radar level meter.

• 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.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.9
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• pp.1840-1846
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• 2011

It is necessary to estimate the Doppler spectrum for each range cell for the extraction of useful information from the return echoes in radar systems used for the remote sensing purpose such as detection of moving targets and weather surveillance. The signal amplitude in the beat frequency band is the important parameter in the detection and tracking of targets. However, strong clutter echoes do exist in most radar operation environments and the system phase noise spreads both the clutter and signal echoes of the target. In this paper, the effects of this system phase noise are analyzed concerning the clutter and the signal beat spectrum. It is shown that the separation capability of adjacent beat signal depends on the degree of spread in the clutter and beat signal caused by the radar system phase noise

• Journal of Navigation and Port Research
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• v.39 no.4
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• pp.307-312
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• 2015

ARPA(Automatic Radar Plotting Aid) is a device to calculate CPA(closest point of approach)/TCPA(time of CPA), true course and speed of targets by vector operation of relative courses and speeds. The purpose of this study is to develop target acquisition and tracking technology for ARPA Radar implementation. After examining the previous studies, applicable algorithms and technologies were developed to be combined and basic ARPA functions were developed as a result. As for main research contents, the sequential image processing technology such as combination of grayscale conversion, gaussian smoothing, binary image conversion and labeling was deviced to achieve a proper target acquisition, and the NNS(Nearest Neighbor Search) algorithm was appllied to identify which target came from the previous image and finally Kalman Filter was used to calculate true course and speed of targets as an analysis of target behavior. Also all technologies stated above were implemented as a SW program and installed onboard, and verified the basic ARPA functions to be operable in practical use through onboard test.

• Journal of Satellite, Information and Communications
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• v.12 no.2
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• pp.1-10
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• 2017

In this paper we propose three existing reflection removal algorithms and one proposed algorithm to estimate accurate targets in near field using IR-UWB (Impulse-Radio Ultra Wideband) radar. The received signal includes unnecessary reflected wave signals to the target signal. A reflective cancellation algorithm was used to remove unnecessary signals and estimate only the correct target signal. The location of the targets is estimated in real time with one transmitting antenna and one receiving antenna. In order to overcome the disadvantages of the existing three reflection removal algorithms, we propose a new reflection removal algorithm and estimate the most accurate target. Also we used DSP(Digital Signal Processor) to install the external mounting of vehicles. This paper will contribute to the study of the future reflections.

• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.178-184
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• 2024

The radar installed on an aircraft has various operating modes depending on tactical purposes, allowing for the configuration of search areas according to each mode's operational objectives. active electronically scanned array (AESA) radar emits search beams sequentially according to a predefined search beam grid within the designated search area specified by the pilot to detect targets within it. It is crucial that the radar can stably search the area designated by the pilot for target detection, even as the aircraft's attitude changes. This paper focuses on stabilizing the search pattern in the air-to-air operational mode of aircraft-mounted radar to ensure stable target detection during roll and pitch maneuvers of the aircraft. The paper demonstrates its performance by simulating aircraft maneuvers and targets in a system integration laboratory (SIL) test environment.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.7
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• pp.560-570
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• 2017

Recently, well-known SAR imaging algorithms have been developed to form the focused SAR images for stationary targets. In general, the conventional methods exploit the range variation only defined by the motion of radar platform and SAR geometry. However, for SAR imaging of ground moving targets, the motion of the targets induces an additional range shift, yielding the blurred SAR images. To overcome the problem, in this paper we propose an effective motion compensation algorithm operated under a multi-channel SAR, named along-track interferometry(ATI) and phase unwrapping to directly estimate the motion parameters of the targets. In simulations, 50 Monte-Carlo simulation results show the effectiveness of the algorithm in the presence of noise.

• 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.