• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1243-1257
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• 2019

As current algorithms unable to perform effective fusion processing of unknown complex radar signals lacking database, and the result is unstable, this paper presents a multi-level fusion processing algorithm for complex radar signals based on evidence theory as a solution to this problem. Specifically, the real-time database is initially established, accompanied by similarity model based on parameter type, and then similarity matrix is calculated. D-S evidence theory is subsequently applied to exercise fusion processing on the similarity of parameters concerning each signal and the trust value concerning target framework of each signal in order. The signals are ultimately combined and perfected. The results of simulation experiment reveal that the proposed algorithm can exert favorable effect on the fusion of unknown complex radar signals, with higher efficiency and less time, maintaining stable processing even of considerable samples.

• Journal of the Korea Institute of Military Science and Technology
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• v.9 no.2 s.25
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• pp.118-126
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• 2006

As weapon systems are becoming advanced and intelligent, they are designed to have such events like ejecting sub-munitions. So it is continually requested to measure the event time and position exactly. We can measure the event time and position by analyzing the complex signal of the instrumentation radar in the time domain and can also obtain the spin rate of the target by analyzing the complex signal in the frequency domain.

• Journal of the Korea Institute of Military Science and Technology
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• v.6 no.4
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• pp.80-88
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• 2003

Radar signal simulation is increasingly gaining in importance according as modem radar systems are more complex. Although computer performance has been advanced, it is difficult to implement the real-time simulation because the detailed model for the radar is necessary to get the desired accuracy. In order to achieve real time operation, we propose radar signal generation technique using ambiguity function, Instead of wellknown correlation method. The ambiguity function is the mathematical modeling of the signal processing procedure which is a simulation section to require the most computations.

• Proceedings of KOSOMES biannual meeting
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• 2005.11a
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• pp.75-78
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• 2005

The detectability of radar depends on RCS(radar cross section). The RCS for complex radar targets may be only approximately calculated by using low-frequency or high-frequency scattering methods, while the RCS for simple radar targets can be exactly obtained by applying on eigen-function method. However, the conventional methods for calculation of RCS are computationally complex. We propose an radar signal model for RCS calculation by MUSIC algorithm In this research, it is assumed that the radar target is considered as a ring of scatterers. The amplitudes of scatterers may be statistically distributed. As the result, the radar signal model is proposed to use MUSIC, and the RCS is calculated by a simple linear algebraic method.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.357-359
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• 2009

In this paper, the 2.4GHz doppler radar system consisting of the doppler radar module and a baseband module were designed to detect heartbeat and respiration signal without direct skin contact. A bio-radar system emits continuous RF signal of 2.4GHz toward human chest, and then detects the reflected signal so as to investigate cardiopulmonary activities. The heartbeat and respiration signals acquired from quadrature signal of the doppler radar system are applied to the pre-processing circuit, amplification circuit, and the offset circuit of the baseband module. ECG(electrocardiogram) and reference respiration signals are measured simultaneously to evaluate the doppler radar system. As a result, the respiration signal of doppler radar signal is detected to 1m without complex digital signal processing. The sensitivity and calculated from I/Q respiration signal were $98.29{\pm}1.79%$, $97.11{\pm}2.75%$, respectively, and positive predictivity were $98.11{\pm}1.45%$, $92.21{\pm}10.92%$, respectively. The sensitivity and positive predictivity calculated from phase and magnitude of the doppler radar were $95.17{\pm}5.33%$, $94.99{\pm}5.43%$, respectively. In this paper, we confirmed that noncontact real-time heartbeat and respiration detection using the doppler radar system has the possibility and limitation.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.25 no.6
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• pp.700-708
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• 2014

Jet engine modulation(JEM) is a frequency modulation phenomenon of the radar signal induced by electromagnetic scattering from a rotating jet engine turbine. Although JEM can be used as a representative radar target recognition method by providing unique information on the target, its recognition performance may be degraded in the observation range of weakly present JEM. Hence, this paper presents a method for extracting the JEM component by decomposing the radar signal into intrisic mode functions(IMFs) via complex empirical mode decomposition(CEMD) and by combining them based on signal eccentricity. Its application to various signals demonstrated that the proposed method improved the clarity of JEM analysis and could extend the effective observation range of JEM.

• Journal of Communications and Networks
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• v.16 no.1
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• pp.92-97
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• 2014

A novel method for extracting frequency slippage signal from radar full pulse sequence is presented. For the radar full pulse sequence received by radar interception receiver, radio frequency (RF) and time of arrival (TOA) of all pulses constitute a two-dimensional information sequence. In a complex and intensive electromagnetic environment, the TOA of pulses is distributed unevenly, randomly, and in a nonstationary manner, preventing existing methods from directly analyzing such time series and effectively extracting certain signal features. This work applies Gaussian noise insertion and structure function to the TOA-RF information sequence respectively such that the equalization of time intervals and correlation processing are accomplished. The components with different frequencies in structure function series are separated using empirical mode decomposition. Additionally, a chaos detection model based on the Duffing equation is introduced to determine the useful component and extract the changing features of RF. Experimental results indicate that the proposed methodology can successfully extract the slippage signal effectively in the case that multiple radar pulse sequences overlap.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.505-508
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• 2000

The success of target reconstruction in SAR(Synthetic Aperture Radar) imaging system is greatly dependent on the coherent detection. Primary causes of incoherent detection are uncompensated target or sensor motion, random turbulence in propagation media, wrong path in radar platform, and etc. And these appear as multiplicative phase error to the echoed signal, which consequently, causes fatal degradations such as fading or dislocation of target image. In this paper, we present iterative phase error estimation scheme which uses echoed data in all temporal frequencies. We started with analyzing wave equation for one point target and extend to overall echoed data from the target scene - The two wave equations governing the SAR signal at two temporal frequencies of the radar signal are combined to derive a method to reconstruct the complex phase error function. Eventually, this operation attains phase error correction algorithm from the total received SAR signal. We verify the success of the proposed algorithm by applying it to the simulated spotlight-mode SAR data.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.6
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• pp.124-132
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• 2011

In a modern electronic warfare, radar signal environments become more denser and complex. Therefor the capability of reliable signal analysis techniques is required for ES(Electronic warfare Support) system to identify and analysis individual emitter signals from received signals. In this paper, we propose the new signal grouping algorithm to ensure the reliable signal analysis and to reduce the cost of the signal processing steps in the ES. The proposed grouping algorithm uses KDE(Kernel Density Estimator) and its CDF(Cumulative Distribution Function) to compose windows considering the statistical distribution characteristics based on the radar frequency modulation type. Simulation results show the good performance of the proposed technique in the signal grouping.