• 대한임베디드공학회논문지
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• 제13권6호
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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.

• 한국자동차공학회논문집
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• 제18권4호
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• pp.54-61
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• 2010

In this paper, we design the signal processing unit to effectively support the proposed algorithm for an automotive Frequency Modulation Continuous Wave(FMCW) radar. In the proposed method, we can obtain the distance and velocity with improved error depending on each range(long, middle, and short) of the target. Since a high computational capacity is required to obtain more accurate distance and velocity for target in near range, the proposed signal processing unit employs the time de-interleaving and the frequency interpolation method to overcome the limitation. Moreover, for real-time signal processing, the parallel architecture is used to extract simultaneously the distance and velocity in each range.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.158.2-158
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• 2001

This paper introduces the design and implementation of a signal processing system for an airborne active homing radar system. This airborne active homing radar system uses the pulse Doppler radar of high PRF (Pulse Repetition Frequency) for computation of exact relative velocity of the target. This system carries out two operations mainly. The first is to transmit and receive microwave signal through the antenna. The second is to calculate the relative velocity of the target taking advantage of the Doppler frequency signal reflected from the target and detect the angle error between a target and an antenna LOS (Line Of Sight) to make the antenna direction coincident with the target. The signal processing system has a role of the latter.

• IEIE Transactions on Smart Processing and Computing
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• 제3권6호
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• pp.372-381
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• 2014

This study examined a radar signal active cancelation technique, which is a theoretical way of achieving stealth by employing a baseband process that involves sampling the incoming hostile radar signal, analyzing its characteristics, and generating countermeasure signals to cancel out the linear-FM signal of the hostile radar signal reflected from the airborne target. To successfully perform an active cancelation, the effects of errors in the countermeasure signal were first analyzed. To generate the countermeasure signal that requires very fast and accurate processing, the down-sampling technique with the suboptimal maximum likelihood estimation (sMLE) scheme was proposed to improve the speed of the estimation process while preserving the estimation accuracy. The simulation results showed that the proposed down-sampling technique using a 2048 FFT size yields substantial power reduction despite its small FFT size and exhibits similar performance to the sMLE scheme using the 32768 FFT size.

• International journal of advanced smart convergence
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• 제9권2호
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• pp.114-122
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• 2020

When replacing hardware due to obsolescence, discontinuation, and expansion of software-equipped electronic equipment, software changes are required in the past, but if virtualization technology is applied, it can be applied without software changes. In this regard, we studied in order to apply virtualization technology in the development of naval multi-function radar signal processing, we studied hardware and OS independency for Docker and performance comparison between Docker and virtual machine. As a result, it was confirmed that hardware and OS independence exist when using Docker and that high-speed processing is possible compared to the virtual machine.

• 한국정보통신학회논문지
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• 제14권12호
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• pp.2628-2635
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• 2010

본 논문에서는 차량용 FMCW(Frequency Modulated Continuous Wave) 레이더의 도플러 주파수와 아날로그-디지털 변환기 비트 수에 따른 탐지 성능 분석 및 신호처리부 개발을 진행하고자 한다. 성능 평가를 위한 FMCW 레이더의 시스템 모델은 송신부와 수신부로 구성되어 있으며 채널은 가우시안 잡음 환경을 사용한다. 이론과 시뮬레이션을 통해서 시스템 모델을 검증한다. 수신부에서는 수신 신호와 기준 신호사이의 부정합으로 인한 주파수 오차가 발생하게 된다. 75cm의 분해능를 갖는 FMCW 레이더에서 도플러 주파수가 약 38KHz이하인 경우 탐지 성능의 열화가 발생하지 않음을 알 수 있다. 아날로그-디지털 변환기 비트에 따른 탐지 성능은 6비트가 최소의 비트로 결정될 수 있음을 알 수 있다. 그리고 FPGA를 이용하여 디지털 송신 파형 발생기를 위한 집적 디지털 신디사이저(Direct Digital Synthesis) 칩을 기반한 FMCW 레이더 신호처리부를 설계 및 구현을 진행한다.

• 한국항행학회논문지
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• 제23권2호
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• pp.142-150
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• 2019

포구속도는 총포탄약의 평가에 있어 가장 중요한 계측항목들 중 하나이다. 포구속도는 포탄이 포구를 떠나는 시점의 속도로 정의된다. 특히, 포구속도는 추진제의 성능과 밀접하게 관련이 있기 때문에 정확한 측정이 요구된다. 포구속도의 측정에는 도플러 레이더가 사용되나, 도플러 레이더 신호의 품질은 시험장 환경에 의존한다. 따라서 본 논문에서는 도플러 레이더의 신호 품질을 악화시키는 클러터를 시험장 구조 및 신호처리 방법의 개선을 통해 제거하는 방법을 제시하였다. 개선된 신호처리 방법의 적용을 위해 도플러 레이더의 원시 도플러 데이터를 획득하는 프로그램을 작성하였다. 시험장 구조 및 신호처리 방법의 개선을 통해 얻어진 속도 데이터에 대한 통계적인 검증으로 제안하는 방법이 기존에 사용하던 방법에 비해 클러터 제거에 효과가 있음을 증명하였다.

• 한국인터넷방송통신학회논문지
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• 제23권5호
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• pp.41-46
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• 2023

레이다 시스템에서 최대 탐지거리를 충족하는 것은 매우 중요하다. 최대 탐지거리를 충족하기 위해서는 레이다 시스템의 수신신호에 대한 민감도가 높아야 한다. 또한 레이다 신호처리기에서 동적 영역이 넓어야 한다. 이러한 요구사항을 충족하기 위해서는 신호처리기 보드가 외부 및 내부 노이즈에 강인하게 설계되어야 한다. 특히 보드 내부에서 여러 스위칭 회로로 인하여 발생되는 잡음이 수신되는 레이다 신호에 영향을 최소화 하기 위한 설계가 필요하다. 본 논문에서는 레이다 시스템 성능을 충족하기 위하여 신호처리기 보드의 요구사항을 도출하고 도출된 요구사항을 충족하기 위한 설계에 대하여 기술한다. 또한 외부에서 입력되어 들어오거나 내부에서 생성되는 노이즈의 영향을 최소화 하기 위한 EMC 설계에 대하여 기술한다. 제작된 보드의 시험을 통하여 확보된 성능을 확인한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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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.

• 대한임베디드공학회논문지
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• 제18권6호
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• pp.277-283
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• 2023

Vital signals provide essential information regarding the health status of individuals, thereby contributing to health management and medical research. Present monitoring methods, such as ECGs (Electrocardiograms) and smartwatches, demand proximity and fixed postures, which limit their applicability. To address this, Non-contact vital signal measurement methods, such as CW (Continuous-Wave) radar, have emerged as a solution. However, unwanted signal components and a stepwise processing approach lead to errors and limitations in heart rate detection. To overcome these issues, this study introduces an integrated neural network approach that combines noise removal, demodulation, and dominant-frequency detection into a unified process. The neural network employed for signal processing in this research adopts a MLP (Multi-Layer Perceptron) architecture, which analyzes the in-phase and quadrature signals collected within a specified time window, using two distinct input layers. The training of the neural network utilizes CW radar signals and reference heart rates obtained from the ECG. In the experimental evaluation, networks trained on different datasets were compared, and their performance was assessed based on loss and frequency accuracy. The proposed methodology exhibits substantial potential for achieving precise vital signals through non-contact measurements, effectively mitigating the limitations of existing methodologies.