• 한국ITS학회:학술대회논문집
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• 2003.11a
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• pp.335-339
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• 2003

• The Science & Technology
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• v.30 no.11 s.342
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• pp.82-83
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• 1997

GPS는 2개 이상의 인공위성에서 발사된 극초단파를 수신하여 이동물체의 위도, 경도, 높이, 표준시간 등을 정확히 파악할 수 있는 최첨단 장비이다. 뛰어난 효능으로 걸프전 때도 각광을 받았던 이 항법시스템은 안개나 사막의 먼지 속에서도 물체의 정확한 위치를 안내해 준다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.7 no.6
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• pp.21-30
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• 2008

The travel speed collected by the prove-car equipped with the GPS has the problems, which are the data's stability and finding out the representative travel speed, by the influence of the traffic signal and etc. at the interrupted traffic. This study was conducted to develop the method of filtering the outlier data from the data collected by the prove-car. The method to remove the outlier data from the serial data which were collected by the prove-car was adapted to each of the quartile deviation statistics model and the management graphic statistics model. The rate of removing the outlier data by the quartile deviation method was $0{\sim}3.7%$ while the rate by the management graphic statistic methods was $0.3{\sim}7.2%$. Both methods show the low removal rate at the dawn time when the traffic is inactivity, on the other hand the remove rate is high during the daytime. However, both methods have the problem such that the threshold level for removing the outlier data was established at the low bound in the case as good as the statistics model. Therefore, it is required for the experience calibration.

• Journal of Korean Society of Transportation
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• v.27 no.2
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• pp.47-56
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• 2009

Effective detection and correction of outliers of raw traffic data collected from the field is of keen interest because reliable traffic information is highly dependent on the quality of raw data. Global positioning system (GPS) based traffic surveillance systems are capable of producing individual vehicle speeds that are invaluable for various traffic management and information strategies. This study proposed a locally weighted regression (LWR) based filtering method for individual vehicle speed data. An important feature of this study was to propose a technique to generate synthetic outliers for more systematic evaluation of the proposed method. It was identified by performance evaluations that the proposed LWR-based method outperformed an exponential smoothing. The proposed method is expected to be effectively utilized for filtering out raw individual vehicle speed data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.1947-1953
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• 2012

The vehicle fuel economy is very important issue in the view of global warming. This paper proposes the three fuel economy improvement algorithms which predict the velocity using altitude data of the positions in front of vehicle and estimates their performances. The proposed 3 algorithms are WMGA(Weighted Mean Gradient Angle), RAADE I, II(Reacceleration After Deacceleration I, II). This research extracts the distance and altitude data from received GPS data and calculates gradient angle and road load for each section. The velocity profile according to proposed algorithms is made for Youngdong highway of 213km. And the test vehicle runs along this highway and fuel economy is measured. RAADE II of proposed algorithms showed better performance by 3.571% in comparison to the conventional CVELCONT3.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.4
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• pp.47-56
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• 2005

In GPS/INS/barometer navigation system for UAV, vertical channel damping loop was introduced to suppress divergence of the vertical axis error of INS, which could be reduced to the level of accuracy of pressure altitude measured by a pitot-static tube. Because static pressure measured by the pitot-static tube depends on the speed and attitude of the vehicle, static pressure error models, based on aerodynamic data from wind tunnel test, CFD analysis, and flight test, were applied to reduce the error of pressure altitude. Through flight tests and sensitivity analyses, the error model using the ratio of differential pressure and static pressure turned out to be superior to the model using only differential pressure, especially in case of high altitude flight. Both models were proposed to compensate the effect of vehicle speed change and used differential and static pressure which could be obtained directly from the output of pressure transducer.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.133-142
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• 2021

Travel pattern recognition is widely used in many aspects such as user trajectory query, user behavior prediction, interest recommendation based on user location, user privacy protection and municipal transportation planning. Because the current recognition accuracy cannot meet the application requirements, the study of travel pattern recognition is the focus of trajectory data research. With the popularization of GPS navigation technology and intelligent mobile devices, a large amount of user mobile data information can be obtained from it, and many meaningful researches can be carried out based on this information. In the current travel pattern research method, the feature extraction of trajectory is limited to the basic attributes of trajectory (speed, angle, acceleration, etc.). In this paper, permutation entropy was used as an eigenvalue of trajectory to participate in the research of trajectory classification, and also used as an attribute to measure the complexity of time series. Velocity permutation entropy and angle permutation entropy were used as characteristics of trajectory to participate in the classification of travel patterns, and the accuracy of attribute classification based on permutation entropy used in this paper reached 81.47%.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.5
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• pp.70-81
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• 2020

Point-by-point data, which is abundantly collected by vehicles with embedded GPS (Global Positioning System), generate useful information. These data facilitate decisions by transportation jurisdictions, and private vendors can monitor and investigate micro-scale driver behavior, traffic flow, and roadway movements. The information is applied to develop app-based route guidance and business models. Of these, speed data play a vital role in developing key parameters and applying agent-based information and services. Nevertheless, link speed values require different levels of physical storage and fidelity, depending on both collecting and reporting intervals. Given these circumstances, this study aimed to establish an appropriate collection interval to efficiently utilize Space Mean Speed information by vehicles with embedded GPS. We conducted a comparison of Probe-vehicle data and Image-based vehicle data to understand PE(Percentage Error). According to the study results, the PE of the Probe-vehicle data showed a 95% confidence level within an 8-second interval, which was chosen as the appropriate collection interval for Probe-vehicle data. It is our hope that the developed guidelines facilitate C-ITS, and autonomous driving service providers will use more reliable Space Mean Speed data to develop better related C-ITS and autonomous driving services.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.475-478
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• 2006

본 논문에서는 소형 무인항공기의 정확한 위치, 속도, 자세 정보를 제공하기 위해 저급의 MEMS IMU를 이용한 항법 시스템을 제안한다. 제안하는 시스템은 비행체의 직선운동과 회전운동을 측정할 수 있는 관성측정기와 위성신호를 수신하여 항체의 위치, 속도 정보를 제공하는 GPS 수신기, 지구 자기장 정보를 이용하여 방향각 정보를 제공하는 지자기 센서들로 구성되었다. SDINS와 약결합 방식의 칼만필터를 이용한 항법 시스템은 초기정렬 알고리즘과 센서 오차 보상 알고리즘, 자력계 보상 알고리즘 및 복합항법 알고리즘으로 나뉘며, 설계된 항법 알고리즘들은 시뮬레이션과 차량 실험을 통해서 성능을 분석하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04d
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• pp.310-312
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• 2003

관성항법 시스템은 항체의 위치, 속도 및 자세정보를 거의 연속적으로 제공할 수 있는 장점이 있다. 그러나 시간의 경과함에 따라 초기오차가 누적되어 발산하게 되는 단점이 있다. 이로 인하여 실제 적용시에는 매우 고가의 정밀한 자이로와 가속도계가 필요하다. 반면 DGPS는 오차의 누적이나 증가없이 장기간 동안 안정적으로 위치정보를 제공하지만 낮은 데이터 전송률과 도심지역과 칼은 곳에서는 신호의 차단이나 전파방해에 영향을 받는 단점이 있다. 이와 같이 상호보완적인 DGPS와 INS 정보를 통합하여 고 정밀의 속도, 위치 및 자세데이터를 제공할 수 있다. 본 논문은 저가의 IMU의 노이즈와 바이어스를 웨이브렛의 soft thresholding 기법을 이용하여 잡음을 제거하여 성능향상을 시도하였다. 통합알고리즘의 필터는 IS차로 구현하였으며 관측치는 DGPS의 위치정보를 이용하였다.