• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.11
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• pp.1785-1796
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• 1993

Thanks to the data processing facilities of modern digital computers, the performances of radar has been promoted greatly as one of the main components of command and control systems along with the computer communications. In this study, radar data integrating and processing systems were designed for the data processing of various information from many kinds of radar in a single data processing system. The performance of the data integrating system was analyzed by applying queueing theory. A radar data integrating network was designed for synchronous relational operations among the information processing systems and the transmission characteristics were also analysed by specific models for each system. The designed data integrating systems can be divided into a simplex type and a distributed multiplex type.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.1
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• pp.99-106
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• 2015

In this study, we propose Radial basis function Neural Network(RBFNN) using Recursive Weighted Least Square Estimation(RWLSE) to effectively deal with big data class meteorological radar data. In the condition part of the RBFNN, Fuzzy C-Means(FCM) clustering is used to obtain fitness values taking into account characteristics of input data, and connection weights are defined as linear polynomial function in the conclusion part. The coefficients of the polynomial function are estimated by using RWLSE in order to cope with big data. As recursive learning technique, RWLSE which is based on WLSE is carried out to efficiently process big data. This study is experimented with both widely used some Machine Learning (ML) dataset and big data obtained from meteorological radar to evaluate the performance of the proposed classifier. The meteorological radar data as big data consists of precipitation echo and non-precipitation echo, and the proposed classifier is used to efficiently classify these echoes.

• Journal of IKEEE
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• v.22 no.3
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• pp.624-629
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• 2018

Classification of moving targets in Pulse Doppler Radar(PDR) for surveillance and reconnaissance purposes is generally carried out based on listening and training experience of Doppler audio signals by radar operator. In this paper, we proposed the automatic classification method to identify the class of moving target with Doppler audio signals using the Mel Frequency Cepstral Coefficients(MFCC) and the Hidden Markov Model(HMM) algorithm which are widely used in speech recognition and the classification performance was analyzed and verified by simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1762-1764
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• 2016

A pulse radar signal is characterized by periodic pulses and noise components between them. In this Letter, we present a test statistic for detecting the presence of a pulse radar which exploits the inherent characteristics of a pulse radar signal by the ratio of maximum power to minimum power from the received signal and compares its sensing performance with that of the energy detector by computer simulation in a variety of situations.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.2
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• pp.147-157
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• 2018

There are increasing demands to provide early warning against intruding drones and cope with potential threats. Commercial anti-drone systems are mostly based on simple target detection by radar reflections. In real scenario, however, it becomes essential to obtain drone radar signatures so that hostile targets are recognized in advance. We present experimental test results that micro-Doppler radar signature delivers partial information on multi-rotor platforms and exhibits limited performance in drone recognition and classification. Afterward, we attempt to generate high resolution profile of flying drone targets. To this purpose, wide bands radar signals are employed to carry out inverse synthetic aperture radar(ISAR) imaging against moving drones. Following theoretical analysis, experimental field tests are carried out to acquire real target signals. Our preliminary tests demonstrate that high resolution ISAR imaging provides effective measures to detect and classify multiple drone targets in air.

• Journal of Satellite, Information and Communications
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• v.9 no.2
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• pp.38-44
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• 2014

This paper presents design of a FMCW (Frequency Modulated Continuous Wave) level meter as well as simulation result of the designed system. The system is designed to measure maximum range of 20m since FMCW radar can be used for measuring short range distance. The distance is measured by analyzing the beat signal which is generated as result of mixing transmitting signal with the reflected received signal. The Fast Fourier Transform is applied to analyze the beat signal for calculating the displacement and Zoom FFT technique is used to minimize measurement error as well as increase the resolution of the measurement. The resolution of the measurement of the designed system in this paper is 2.2mm and bandwidth of 1.024GHz is used for simulation. Thus the simulation results are analyzed and compared in various conditions in order to get a comprehensive idea of frequency resolution and displacement resolution.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.1-10
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• 2019

In this paper, we propose the track initiation algorithm based on the weighted score for TWS radar tracking. This algorithm utilizes radar velocity information to calculate the probabilistic track score and applies the Non-Maximum-Suppression(NMS) to confirm the targets to track. This approach is understood as a modification of a conventional track initiation algorithm in a probabilistic manner. Also, we additionally apply the weighted Hough transform to compensate a measurement error, and it helps to improve the track detection probability. We designed the simulator in order to demonstrate the performance of the proposed track initiation algorithm. The simulation result show that the proposed algorithm, which reduces about 40 % of a false track probability, is better than the conventional algorithm.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.1
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• pp.25-32
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• 2022

IR-UWB radar has been regarded as the most promising technology for non-contact respiration and heartbeat monitoring because of its ability of detecting slight motion even in submillimeter range. Measuring heart rate is most challenging since the chest movement by heartbeat is quite subtle and easily interfered with by a random body motion or background noise. Additionally, periodic sampling can be limited by the performance of computer that handles the radar signals. In this paper, we deploy Lomb-Scargle periodogram method that estimates heart rate even with irregularly sampled data and uneven signal amplitude. Lomb-Scargle periodogram is known as a method for finding periodicity in irregularly-sampled and noisy data set. We also implement a motion detection scheme in order to make the heart rate estimation pause when a random motion is detected. Our scheme is implemented using Novelda's X4M03 radar development kit and its corresponding drivers and Python packages. Experimental results show that the estimation with Lomb-Scargle periodogram yield more accurate heart rate than the method of measuring peak-to-peak distance.

• Journal of IKEEE
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• v.25 no.4
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• pp.703-709
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• 2021

As drone technology advances, the risks of drones are increasing, then technology to detect drones is becoming important. In this thesis, it was verified that miniaturized and lightweighted active array antenna could be used for radar system to detect drones in reality. The transmit-receive module was designed in the form of tile-type to simplify interconnections between devices. The waveform generation module and the down conversion module were miniaturized to include in one body too. As a result of verifing the detection performance through test, it was confirmed that the detection range was over 3.7Km.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.319-328
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• 2024

Semi-supervised learning is a good way to train a classification model using a small number of labeled and large number of unlabeled data. We applied semi-supervised learning to a synthetic aperture radar(SAR) image classification model with a limited number of datasets that are difficult to create. To address the previous difficulties, semi-supervised learning uses a model trained with a small amount of labeled data to generate and learn pseudo labels. Besides, a lot of number of papers use a single fixed threshold to create pseudo labels. In this paper, we present a semi-supervised synthetic aperture radar(SAR) image classification method that applies different thresholds for each class instead of all classes sharing a fixed threshold to improve SAR classification performance with a small number of labeled datasets.