• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.10
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• pp.541-546
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• 2020

The surveillance radar for naval ships is an essential equipment of a battle system that executes the detection and tracking of targets, and the shooting support function; it calculates the three-dimensional track of the target range, azimuth, and altitude to carry out its duty. The surveillance radar consists of an antenna, a transceiver, a processing unit, and an air dryer section. The radar radiates the transmission signal on the antenna section, receives the reflected signal from the target, and amplifies the signals on the transceiver section. The signal received from the antenna is used to provide the operator with target information in various ways. This study identified the display problems when the information about the target is displayed through the radar. The causes of the problems were analyzed and improved. The tracking disappearance phenomenon caused by the altered-course of the ship was improved on the TWS tracking algorithm. The validity of the improved TWS tracking algorithm was confirmed by the normal condition of the target status on the B-scope.

• The Journal of the Acoustical Society of Korea
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• v.43 no.2
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• pp.225-233
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• 2024

Active sonar transmits sound waves to detect covertly maneuvering underwater objects and detects the signals reflected back from the target. However, in addition to the target's echo, the active sonar's received signal is mixed with seafloor, sea surface reverberation, biological noise, and other noise, making target recognition difficult. Conventional techniques for detecting signals above a threshold not only cause false detections or miss targets depending on the set threshold, but also have the problem of having to set an appropriate threshold for various underwater environments. To overcome this, research has been conducted on automatic calculation of threshold values through techniques such as Constant False Alarm Rate (CFAR) and application of advanced tracking filters and association techniques, but there are limitations in environments where a significant number of detections occur. As deep learning technology has recently developed, efforts have been made to apply it in the field of underwater target detection, but it is very difficult to acquire active sonar data for discriminator learning, so not only is the data rare, but there are only a very small number of targets and a relatively large number of non-targets. There are difficulties due to the imbalance of data. In this paper, the image of the energy distribution of the detection signal is used, and a classifier is learned in a way that takes into account the imbalance of the data to distinguish between targets and non-targets and added to the existing technique. Through the proposed technique, target misclassification was minimized and non-targets were eliminated, making target recognition easier for active sonar operators. And the effectiveness of the proposed technique was verified through sea experiment data obtained in the East Sea.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.172-180
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• 2012

In multi-static sonar for detecting an underwater target, received signals contain the target echo, reverberation and clutter. Clutter and reverberation are main causes of increasing the false alarm rate. MRAL classifies received signals according to the spatial similarity, and it regards classified signal as reflected signals from a reflector. MRAL reduces the false alarm rate this way. However, the results of MRAL can have localization errors. In this paper, an MRAL post processing algorithm is proposed to reduce the localization errors with the least square (LS) method.

• Journal of Advanced Navigation Technology
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• v.13 no.2
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• pp.187-193
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• 2009

A lens which is one of cores for the high precision laser range finder is utilized to compute the distance by measuring the phase displacement. In order to measure the phase displacement, we transmit the optical signal from the laser diode to a target and receive the reflected laser light from the target. In this paper, we propose new lens design technique to solve the problem due to the inconsistent curvature of the lens, which consistently collects optical signals and performs the transmission and reception of the optical data, and test the implementation of the laser range finder based on the proposed technique. Since the proposed laser range finder has low error rate comparing to the conventional techniques, it may be apply to the high precision distance measurement.

• Journal of the Korea Society for Simulation
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• v.24 no.4
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• pp.23-28
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• 2015

The detection field is an important sector of the factors influencing the battle field. Basically, The radar emits a radio wave to perform the detection in the existing way. However, When most existing radars identify target by signal processing to return radio wave, Environmental attenuation factor does not reflected. The radar using this radio wave has got the possibility changing detect result depending on attenuation factor by environmental conditions, The operational problems may arise in a real battle field. Therefore, In this paper, When emitted radio waves were come back, Reflecting the environmental attenuation factor, Experimental attempts to identify the target to enable more accurately.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.209-213
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• 2006

In this paper describes image enhancement technique using deconvolution processing for ultrasonic nondestructive testing. . When flaws are detected for B-scan or C-scan, blurring effect which is caused by the moving intervals of transducer degrades the quality of images. In addition, acquisited images suffer form speckle noise which is caused by the ultrasonic components reflected from the grain boundary of material [1,2]. The deconvolution technique can restore sharp peak value or clean image from blurring signal or image. This processing is applied to C-scan image obtained from known specimen. Experimental results show that the deconvolution processing contributes to get improved the quality of C-scan images.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.2
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• pp.146-153
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• 2013

The IR-UWB radar system radiates a pulse whose width is several hundred pico sec at Tx antenna and check the time to receive the pulse that reflected from target to measure the TOA. In this paper, we present a new algorithm which supplement the conventional ranging algorithm for more accurate estimation. We get received signal data using IR-UWB Radar module which equipped a NVA6000 UWB Transceiver and analysis the data of multi-path. Consequently, we found the property of UWB multi-path signal, which best fit a Gamma distribution. so we present a algorithm using Gamma-distribution and compared a performance with conventional ranging algorithm.

• 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.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.9
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• pp.930-937
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• 2014

Inverse synthetic aperture radar (ISAR) images can be generated by radar which radiates the electromagnetic wave to a target and receives signal reflected from the target. ISAR images can be widely used to target detection and recognition. This paper proposed a method of generation of high resolution ISAR images by synthesizing frequency spectrums of each stepped chirp waveform in one burst and sub-sampling in frequency domain. This process is performed over entire bursts during coherent processing interval. Conventional ISAR image generation method using stepped frequency waveform has a severe problem of short unambiguous range, loading to ghost phenomenon. However, this problem can be resolved by the proposed method. In simulations, we generate high resolution ISAR image of the moving target which is Boeing-737 aircraft model composed of several ideal point scatterers.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.104-113
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• 2022

In this paper, we propose deep learning-based target distance and velocity estimation technique for OFDM radar systems. In the proposed technique, the 2D periodogram is obtained via 2D fast Fourier transform (FFT) from the reflected signal after removing the modulation effect. The periodogram is the input to the conventional and proposed estimators. The peak of the 2D periodogram represents the target, and the constant false alarm rate (CFAR) algorithm is the most popular conventional technique for the target's distance and speed estimation. In contrast, the proposed method is designed using the multiple output convolutional neural network (CNN). Unlike the conventional CFAR, the proposed estimator is easier to use because it does not require any additional information such as noise power. According to the simulation results, the proposed CNN improves the mean square error (MSE) by more than 5 times compared with the conventional CFAR, and the proposed estimator becomes more accurate as the number of transmitted OFDM symbols increases.