• 대한공간정보학회지
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• 제23권4호
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• pp.49-55
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• 2015

본 연구에서는 GPS/GLONASS/BDS 통합 측위를 수행하기 위해 고려해야 할 사항을 소개하였으며, 저가형 수신기를 통해 개활지 환경과 난수신 환경에서의 통합 측위의 정확도를 분석하였다. 개활지 환경에서는 통합 측위 시 수평 RMSE가 1.2m로 단일 시스템만을 이용한 측위에 비해 수평 정확도가 17-55%만큼 향상되었으며 편향이 개선되어 높은 측위 성능을 나타내는 것을 확인하였다. 난수신 환경에서의 가시 위성 개수를 파악한 결과 단일 시스템을 이용하여 측위를 할 때에는 가시 위성의 개수가 4개 미만이 되어 측위가 되지 않는 경우가 발생했으나, 통합 측위를 할 때에는 가시 위성 개수가 항상 4개 이상이 되어 측위가 되지 않는 경우가 발생하지 않았다. 난수신 환경에서 통합 측위의 수평 RMSE는 6.4m로 단일 시스템만을 이용하여 측위를 수행했을 때보다 8-47%만큼 수평 정확도가 향상되는 것을 확인하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2005년도 Proceedings of ISRS 2005
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• pp.349-352
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• 2005

The telematics positioning testbed is an infrastructure to test and verify positioning technology, the sub-component of telernatics system. The positioning testbed provides the environment of performance analysis for acquisition of static and dynamic positioning information using telematics vehicle. This testbed consists of onboard positioning system, positioning reference station and lab positioning server. The onboard positioning system equipped in telematics vehicle, consists of target positioning system, reference positioning system, and analysis tool. A equipment acquiring high precision positioning data obtained from GPS combined with IMU was set as a reference positioning system. Analysis tool compares observed positioning data with high precision positioning information from a reference positioning system, and processes positioning information. Positioning reference station is RTK system used for reducing atmosphere error, and it transmits corrected information to reference positioning system. Positioning server which is located at laboratory manages positioning database and provides monitoring data to integrated testbed operating system. It is expected that the testbed supports commercialization of telernatics technology and services, integrated testing among component technology and verification.

• Journal of Positioning, Navigation, and Timing
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• 제9권4호
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• pp.387-395
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• 2020

Accuracy of an integrated Global Positioning System (GPS) / Inertial Navigation System (INS) relies heavily on the visibility of GPS satellites. Especially, its accuracy is dramatically degraded in urban canyon due to signal obstructions due to large structures. In this paper, we propose a new integrated positioning system that effectively combines INS, GPS, ultrasonic sensor, and barometer in GPS-denied environments. In the proposed system, the ultrasonic sensor provides velocity information along the forward direction of moving vehicle. The barometer output provides height information compensated for the pressure variation due to fast vehicle movements. To evaluate the performance of the proposed system, an experiment was carried out by mounting the proposed system on a test car. By the experiment result, it was confirmed that the proposed system bears good potential to maintain positioning accuracy in harsh urban environments.

• Journal of Positioning, Navigation, and Timing
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• 제8권1호
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• pp.31-40
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• 2019

In recent years, Intelligent Transport Systems (ITS) and Autonomous Vehicle Technology have actively studied around the world. In order to achieve the purpose of Advanced Driver Assistance System (ADAS) and Autonomous Vehicle Technology, it must be obtained accurate and reliable positioning. However, the problem of positioning in the urban area is a low position accuracy caused by the reduction of the number of visible satellites due to high buildings. In this paper, we analyzed the availability of precise positioning system in urban area are using GPS/BDS integrated system. For this study, GPS and BDS satellite signals were collected using two low-cost receivers in the open sky and a designed software based platform for precise positioning performance analysis. And we analyzed the precise positioning performance by changing the mask angle considering the urban area. From the results, it can be confirmed that the performance of precise positioning of GPS only and BDS only decrease in the environment where mask angle is $40^{\circ}$ to $45^{\circ}$, however, GPS/BDS integrated system maintains high performance of precise positioning.

• 한국정보시스템학회지:정보시스템연구
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• 제17권1호
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• pp.131-153
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• 2008

So many location based service systems, including automobile navigation system logistic management, taxi fleet management, and so on, are being used everywhere. However, these are all outdoors. This paper provides a stepping stone for commercial indoor location based services by developing an integrated system of our indoor positioning and map viewer modules. For the indoor positioning, we propose WLAN (Wireless Local Area Network) based EKF (Extended Kalman Filter) which estimates user's current location and tracts user's trace in the sequence of time. Our map viewer renders a map recorded in an Autocad DXF file and provides functions of map manipulation such as zoom-in, zoom-out, and move. We integrate our indoor positioning and map viewer modules and discuss the experimental results of the integrated system.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.946-948
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• 2003

This paper describes the development of an INS (Inertial Navigation System) integrated positioning system, which can monitor and track the firefighter's position for assisting effective fire-fighting activity and rescue services. The INS consists of highly accurate three-axis gyro sensor and three-axis accelerometer. By integrating an INS to positioning system, it is also possible to obtain the information of firefighter's physical state (e.g. standing, collapse and crouch) of posture including velocity. Consequently, this research would obviously make a contribution to effective rescue activities and safety of firefighters. Besides, this paper presents results from field tests conducted at Tokyo University demonstrating its viability and utility. We also summarize the overall system requirements and architecture, and describe the hardware and software used in the prototype system in detail.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.

• Journal of Positioning, Navigation, and Timing
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• 제13권1호
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• pp.45-52
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• 2024

As part of the Global Positioning System (GPS) modernization program in the United States, Civil Navigation (CNAV) and CNAV-2 messages were developed to introduce flexibility and modern features to the Legacy Navigation (LNAV) message. This paper explores the additional parameters introduced in CNAV/CNAV-2 compared to LNAV, focusing on their roles from the user's perspective. This paper compares the structural and parameter differences among LNAV, CNAV, and CNAV-2. Additionally, we analyze the types and roles of parameters newly incorporated into CNAV/CNAV-2 that were absent in LNAV.

• Journal of Positioning, Navigation, and Timing
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• 제12권2호
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• pp.149-155
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• 2023

In urban areas it can be difficult to utilize global navigation satellite systems (GNSS) due to signal reflections and blockages. It is thus crucial to detect reflected or blocked signals because they lead to significant degradation of GNSS positioning accuracy. In a previous study, a classifier for global positioning system (GPS) signal reception conditions was developed using three features and the support vector machine (SVM) algorithm. However, this classifier had limitations in its classification performance. Therefore, in this study, we developed an improved machine learning based method of classifying GPS signal reception conditions by including an additional feature with the existing features. Furthermore, we applied various machine learning classification algorithms. As a result, when tested with datasets collected in different environments than the training environment, the classification accuracy improved by nine percentage points compared to the existing method, reaching up to 58%.

• Journal of Positioning, Navigation, and Timing
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• 제7권2호
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• pp.61-71
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• 2018

Existing radio positioning systems have a drawback that the attitude of user's tag is difficult to be determined. Although forward link angle of arrival (FLAOA) technology that uses measurements of array antenna arranged in a tag among the angle of arrival (AOA) technologies can estimate attitude and positioning of tags, it cannot extend the estimated results into three-dimensional (3D) results due to complex non-linear model displayed because of the effects of 3D positioning and attitude in tags. This paper proposed a radio navigation technique that determines 3D attitude and positioning via FLAOA / time of arrival (TOA) integration. According to the order of determining attitude and positioning, two integration techniques were proposed. To analyze the performance of the proposed technique, MATLAB-based simulations were used to verify the performance. The simulation results showed that the first proposed method, TOA-FLAOA integrated technique, showed about 0.15 m of positioning error, and $2-3^{\circ}$ of attitude error performances regardless of the positioning space size whereas the second method, differenced FLAOA-TOA integrated technique, revealed a problem that a positioning error became larger as the size of the positioning space became larger.