• 대한임베디드공학회논문지
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• 제6권5호
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• pp.319-325
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• 2011

Recently, collision avoidance systems are under development to reduce the traffic accidents and driver comfort for automotive radar. Pulse radar can detect their range and velocities of moving vehicles using range gate and FFT(Fast Fourier Transform) of the doppler frequency. We designed the real time DSP(Digital Signal Processing) based automotive UWB(Ultra Wideband) radar, and implemented DSP to detect the range and velocity within 100ms for real time system of the automotive UWB radar. We also measured the range and velocity of a moving vehicle using designed automotive UWB radar in a real road environment.

• 한국위성정보통신학회논문지
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• 제9권3호
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• pp.53-58
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• 2014

국제적인 차량용 레이더는 22~26GHz 대역을 사용할 수 있도록 규정하고 있으나, 최근 전반적으로 위 주파수대역은 이용을 종료하는 추세이며, 차량의 사각지대 및 보행자 감지용 고해상도 차량용 레이더 도입을 위해 밀리미터파 대역 중 77~81GHz대역의 광대역 레이더로 전환할 예정이다. 그러나 현재 국제적으로 71~275GHz대역은 전파천문우주 연구를 위한 업무로 사용하도록 규정되어 있다. 이에 따라 위 대역의 차량용 레이더와 국내 전파천문업무 사이에 간섭이 있을 것으로 예상되어 간섭영향을 분석하고 보호이격거리를 도출하였다.

• 한국자동차공학회논문집
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• 제22권7호
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• pp.31-39
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• 2014

Recently, as Intelligent Transportation System (ITS) and self-driving system become influential in the ground transportation system, automotive radar systems have been actively studied among the various radar systems to implement the vehicle collision detection system and distance measurement system between vehicles. Most of the automotive radars are Frequency Modulated Continuous Wave (FMCW) radar type which can calculate distance and velocity of target by estimating the frequency difference between the transmitted signal and received signal. Therefore, accurate frequency estimation is very important in the FMCW radar system. For this reason, to improve the measurement accuracy of the FMCW radar, Reverse Directional FRI (RD-FRI) Super-Resolution technique which has high frequency estimation accuracy is applied to the FMCW radar system. The feasibility of the proposed technique is evaluated with simulation results and compared with FFT and conventional Super-Resolution techniques. The simulation results show that the proposed technique estimates the frequency with high accuracy and the distance with centimeter accuracy.

• 한국멀티미디어학회논문지
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• 제22권12호
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• pp.1430-1437
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• 2019

In this paper, we proposed a methodology for estimating rainfall intensity using a W-band FMCW automotive radar signal which is the core technology of autonomous driving car. By comparing and analyzing the results of rainfall and non-rainfall observation, we found that the reflection intensity of the automotive radar is changed with rainfall intensity. We could confirm the possibility of deriving the quantitative precipitation estimation using the methodology derived from this result. In addition it can be possible to develop a new paradigm of precipitation observation technique by observing various events together with the weather radar and the ground rainfall observation equipment.

• 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.

• 한국자동차공학회논문집
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• 제25권1호
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• pp.92-102
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• 2017

For an automotive surveillance radar system, fast-chirp train based FMCW (Frequency Modulated Continuous Wave) radar is a very effective method, because clutter and moving targets are easily separated in a 2D range-velocity map. However, pedestrians with low echo signals may be masked by strong clutter in actual field. To address this problem, we proposed in the previous work a clutter cancellation and moving target indication algorithm using the coherent phase method. In the present paper, we initially composed the test set-up using a 24 GHz FMCW transceiver and a real-time data logging board in order to verify this algorithm. Next, we created two indoor test environments consisting of moving human and stationary targets. It was found that pedestrians and strong clutter could be effectively separated when the proposed method is used. We also designed and implemented these algorithms in FPGA (Field Programmable Gate Array) in order to analyze the hardware and time complexities. The results demonstrated that the complexity overhead was nearly zero compared to when the typical method was used.

• 한국산업정보학회논문지
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• 제28권1호
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• pp.1-8
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• 2023

본 논문은 ISO 17387규격에서 제공하고 있는 차선변경 보조시스템(LCDAS)에 기반을 둔 중단거리 차량용 레이더 시스템을 개발하였다. 사용한 규격에서는 지능형 이동 시스템에서 사용하고 있는 성능 요구사항과 시험 과정에 대해 기술되어 있다. 중단거리 차량용 레이더 시스템은 최대 80m까지 목표물을 검출할 수 있으며, 갱신주기는 50ms로 단축할 수 있도록 설계하였다. 또한, ROI 전처리 기술을 활용하여 신호처리에서의 계산량을 대폭 감소시킬 수 있었다. 최종적으로, 실제 운전 상황을 설정하여 실제 시험을 수행하였으며, 두 가지의 시나리오를 설정하여 성능을 평가했다.

• 자동차안전학회지
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• 제10권2호
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• pp.29-35
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• 2018

This paper presents a quantitative evaluation method and result of moving vehicle perception using automotive radar. It is also important to analyze the accuracy of the perception algorithm quantitatively as well as to accurately percept nearby moving vehicles for safe and efficient autonomous driving. In this study, accuracy of the automotive radar-based perception algorithm which is developed based on interacting multiple model (IMM) has been verified via vehicle tests on real roads. In order to obtain experimental data for quantitative evaluation, Long Range Radar (LRR) has been mounted on the front of the ego vehicle and Short Range Radar (SRR) has been mounted on the rear side of both sides. RT-range has been installed on the ego vehicle and the target vehicle to simultaneously collect reference data on the states of the two vehicles. The experimental data is acquired in various relative positions and velocity, and the accuracy of the algorithm has been analyzed according to relative position and velocity. Quantitative analysis is conducted on relative position, relative heading angle, absolute velocity, and yaw rate of each vehicle.

• 대한임베디드공학회논문지
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• 제6권1호
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• pp.16-24
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• 2011

Automotive Radar Systems are currently under development for various applications to increase accuracy and reliability. The target tracking is most important in single or multiple target environments for accuracy. The tracking algorithm provides smoothed and predicted data for target position and velocity(Doppler). To this end, the fixed gain filter(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter) and dynamic filter(Kalman filter, Singer-Kalman filter, etc) are commonly used. Gating is used to decide whether an observation is assigned to an existing track or new track. Gating algorithms are normally based on computing a statistical error distance between an observation and prediction. The data association takes the observation-to-track pairings that satisfied gating and determines which observation-to-track assignment will actually be made. For data association, NNPDA(Nearest Neighbor Probabilistic Data Association) algorithm is proposed. In this paper, we designed a target tracking system developed for an Automotive Radar System. We show the experimental results of the 77GHz FMCW radar sensor on the roads. Four tracking algorithms(${\alpha}{\beta}$ filter, ${\alpha}{\beta}{\gamma}$ filter, 2nd order Kalman filter, Singer-Kalman filter) have been compared and analyzed to evaluate the performance in test scenario.

• 제어로봇시스템학회논문지
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• 제22권8호
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• pp.633-642
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• 2016

It is important for advanced active safety systems and autonomous driving cars to get the accurate estimates of the nearby vehicles in order to increase their safety and performance. This paper proposes a sensor fusion method for radar and vision sensors to accurately estimate the state of the preceding vehicles. In particular, we performed a study on compensating for the lateral state error on automotive radar sensors by using a vision sensor. The proposed method is based on the Interactive Multiple Model(IMM) algorithm, which stochastically integrates the multiple Kalman Filters with the multiple models depending on lateral-compensation mode and radar-single sensor mode. In addition, a Probabilistic Data Association Filter(PDAF) is utilized as a data association method to improve the reliability of the estimates under a cluttered radar environment. A two-step correction method is used in the Kalman filter, which efficiently associates both the radar and vision measurements into single state estimates. Finally, the proposed method is validated through off-line simulations using measurements obtained from a field test in an actual road environment.