• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2005년도 공동학술대회 논문집
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• pp.215-220
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• 2005

지뢰탐지에 효과적으로 적용하기 위해 광전계 센서를 이용한 GPR 시스템을 개발하였다. 측정되는 전기장의 왜곡을 최소화 하는 광전계 센서는 크기와 무게가 매우 적어 측정 장치로 운용하기가 용이하므로 지뢰탐지와 같이 세밀한 주의가 요구되는 곳에 적합하다. 송수신 장치 역할을 하는 벡터 네트웍 분석기와 광신호 발생기와 광검파기를 탑재한 광변조기, double ridge horn 송신 안테나와 광전계 센서로 구성된 stepped frequency radar 시스템이 개발되었으며, 이 시스템의 매우 긴 측정시간의 단점을 극복하기 위해 동일한 수준의 S/N 비를 가지는, 임펄스 발생기와 오실로스코프로 구성된 impulse radar 시스템이 또한 개발되었다. 비교 결과 자료 수준은 거의 동일하나 측정시간은 스텝 측정의 경우 8배 이상 빨라진 것을 알 수 있었으며, 동일한 자료를 연속 측정으로 획득한 결과 100 배 이상 빨라 질 수 있음을 확인하였다. 또한 이 임펄스 레이다 시스템을 PMN2 지뢰모형에 대해 현장과 비슷한 환경에서의 실험실 실험에 적용하여 지뢰모형의 영상을 획득하였다.

• 한국정보통신학회논문지
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• 제25권4호
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• pp.611-614
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• 2021

In this paper, we propose a widerange array microphone for lecturer tracked with Frequency Modulated Continuous Waveform (FMCW) radar sensor. Time Difference-of-Arrival (TDoA) is often used as audio tracking, but the tracking accuracy is poor because the frequency of the voice is low and the relative frequency change is large. FMCW radar has a simple structure and is used to detect obstacles for vehicles, and the resolution can be archived to several centimeter. It is shown that the sensor is useful for detecting a speaker in open area such as a lecture, and we propose an wide range 4-element array microphone beamforming system. Through some experiments, the proposed system is able to adequately track the location and showed a 8.6dB improvement over the selection of the best microphone.

• 한국전자통신학회논문지
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• 제4권2호
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• pp.75-81
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• 2009

본 논문에서는 10.525GHz에서 동작하는 마이크로파 도플러 레이더 센서를 설계/제작하였다. 움직이는 물체를 이용한 측정을 통해 도플러 효과에 따른 센서의 특성을 연구 분석하였으며, 측정 결과를 통해 물체의 속도와 물체와 센서 사이의 거리에 따라 특성에 주파수의 차이를 보임을 알 수 있었다. 측정된 결과를 이용해 마이크로파 도플러 레이더 센서의 FMCW 신호에 의하여 댁내 보안탐지와 같은 용도로 활용할 수 있다.

• 대기
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• 제24권3호
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• pp.433-444
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• 2014

Although the Radar-AWS Rainrate (RAR) calculation system operated by Korea Meteorological Administration estimated precipitation using 2-dimensional composite components of single polarization radars, this system has several limitations in estimating the precipitation accurately. To to overcome limitations of the RAR system, the Korea Meteorological Administration developed and operated the RMQ (Radar-based Multi-sensor Quantitative Precipitation Estimation) system, the improved version of NMQ (National Mosaic and Multi-sensor Quantitative Precipitation Estimation) system of NSSL (National Severe Storms Laboratory) for the Korean Peninsula. This study introduced the RMQ system domestically for the first time and verified the precipitation estimation performance of the RMQ system. The RMQ system consists of 4 main parts as the process of handling the single radar data, merging 3D reflectivity, QPE, and displaying result images. The first process (handling of the single radar data) has the pre-process of a radar data (transformation of data format and quality control), the production of a vertical profile of reflectivity and the correction of bright-band, and the conduction of hydrid scan reflectivity. The next process (merger of 3D reflectivity) produces the 3D composite reflectivity field after correcting the quality controlled single radar reflectivity. The QPE process classifies the precipitation types using multi-sensor information and estimates quantitative precipitation using several Z-R relationships which are proper for precipitation types. This process also corrects the precipitation using the AWS position with local gauge correction technique. The last process displays the final results transformed into images in the web-site. This study also estimated the accuracy of the RMQ system with five events in 2012 summer season and compared the results of the RAR (Radar-AWS Rainrate) and RMQ systems. The RMQ system ($2.36mm\;hr^{-1}$ in RMSE on average) is superior to the RAR system ($8.33mm\;hr^{-1}$ in RMSE) and improved by 73.25% in RMSE and 25.56% in correlation coefficient on average. The precipitation composite field images produced by the RMQ system are almost identical to the AWS (Automatic Weather Statioin) images. Therefore, the RMQ system has contributed to improve the accuracy of precipitation estimation using weather radars and operation of the RMQ system in the work field in future enables to cope with the extreme weather conditions actively.

• 한국ITS학회 논문지
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• 제16권2호
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• pp.140-152
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• 2017

본 논문은 레이더 센서, 비전 센서를 활용한 다중 센서 융합 기반 움직임 검지에 관한 연구를 다룬다. 레이더 센서는 다량의 물체를 검지함에 있어 센서 자체의 움직임이 발생할 경우 주변건물이나 주변 가로수와 같은 사물 혹은 물체를 차량으로 오인하는 경우가 생긴다. 비전 센서의 경우 저렴하고 가장 많이 쓰는 형태이지만 빛, 흔들림, 날씨, 조도 등 외부환경에 취약하다는 문제점이 있다. 각 센서 간의 문제점을 보완하고자 센서 융합을 통한 움직임 검지를 제안하게 되었고 실험환경 내에서 매우 우수한 검지율을 보이게 되었다 센서 간 융합에서 좌표 통일문제와 실시간 전송문제 등을 해결하였으며, 각 센서 간 필터링을 통한 비가공데이터(raw data)의 신뢰성을 높였다. 특히 영상에서는 가우시안 혼합모델(GMM, Gaussian Mixture Model)을 사용하여 레이더 센서의 단점을 극복하고자 했다.

• 자동차안전학회지
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• 제9권4호
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• pp.32-37
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• 2017

For automated vehicles, the integrity and fault tolerance of environment perception sensor have been an important issue. This paper presents radar, vision, lidar(laser radar) fusion-based fault detection algorithm for autonomous vehicles. In this paper, characteristics of each sensor are shown. And the error of states of moving targets estimated by each sensor is analyzed to present the method to detect fault of environment sensors by characteristic of this error. Each estimation of moving targets isperformed by EKF/IMM method. To guarantee the reliability of fault detection algorithm of environment sensor, various driving data in several types of road is analyzed.

• 제어로봇시스템학회논문지
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• 제16권7호
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• pp.639-645
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• 2010

This paper presents the sensor fusion algorithm that recognizes a primary vehicle by fusing radar and monocular vision data. In general, most of commercial radars may lose tracking of the primary vehicle, i.e., the closest preceding vehicle in the same lane, when it stops or goes with other preceding vehicles in the adjacent lane with similar velocity and range. In order to improve the performance degradation of radar, vehicle detection information from vision sensor and path prediction predicted by ego vehicle sensors will be combined for target classification. Then, the target classification will work with probabilistic association filters to track a primary vehicle. Finally the performance of the proposed sensor fusion algorithm is validated using field test data on highway.

• 한국인터넷방송통신학회논문지
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• 제22권6호
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• pp.43-49
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• 2022

FMCW레이다는 일반 차량의 충돌방지용도 뿐만 아니라 자율주행시스템에서 활발히 센서로서 사용이 되고 있다. 본 논문에서는 자율주행센서로서 개발한 2-chip 기반의 FMCW MIMO(Multi Input Multi Output) 레이다 설계 및 구현에 대해서 설명하였다. 사용 칩을 이용하여 48채널의 가상배열을 이용하여 방위각 해상도가 우수하게 설계하였으며, 특히 Frame 기반과 Chirp 기반의 파형발생 및 신호처리를 혼합하여 최대탐지 가능 속도와 속도 보상에 대해 강점을 보유할 수 있도록 제작하였으며, 구현된 시스템은 실험실 내 시험과 실제 주행시험을 통하여 성능 및 상용화 가능성에 대한 분석을 진행하였다.

• Journal of Biosystems Engineering
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• 제25권3호
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• pp.233-240
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• 2000

A commercially available Doppler radar was modified and evaluated for on-board monitoring of ground speed. The radar output was corrected for pitch angle of aircraft based on the output of an electrolytic tilt sensor. The effects of aircraft speed, height and mounting angle on error in the ground speed were evaluated. The speed error decreased with an increase of the mounting angle since the radar contact angle with respect to the ground approached to the mounting angle. The error increased with an increase of the nominal aircraft speed. The altitude insignificantly affected the speed error. The Doppler radar provided acceptable percent errors within 5% in most measurements. The error can be reduced within ${\pm}$1.5% by increasing the mounting angle ($43^{\circ}$). The error of -3.8% at the mounting angle of $29^{\circ}$could be reduced by adjusting the mounting angle with respect to the radar contact angle.

• International journal of advanced smart convergence
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• 제12권4호
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• pp.190-201
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• 2023

Securing transportation safety infrastructure technology for Lv.4 connected autonomous driving is very important for the spread of autonomous vehicles, and the safe operation of level 4 autonomous vehicles in adverse weather has limitations due to the development of vehicle-only technology. We developed the radar-enabled AI convergence transportation entities detection system. This system is mounted on fixed and mobile supports on the road, and provides excellent autonomous driving situation recognition/determination results by converging transportation entities information collected from various monitoring sensors such as 60GHz radar and EO/IR based on artificial intelligence. By installing such a radar-enabled AI convergence transportation entities detection system on an autonomous road, it is possible to increase driving efficiency and ensure safety in adverse weather. To secure competitive technologies in the global market, the development of four key technologies such as ① AI-enabled transportation situation recognition/determination algorithm, ② 60GHz radar development technology, ③ multi-sensor data convergence technology, and ④ AI data framework technology is required.