• Proceedings of the KSRS Conference
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• 2005.10a
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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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• v.9 no.3
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• pp.151-156
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• 2020

A technology for calculating the position of a device is very important for users who receive positioning services, regardless of various indoor/outdoor or with/without any positioning infrastructure existence environments. One of the positioning resources widely used at present, LTE, is a typical infrastructure that can overcome the space limitation, however its positioning method based on the position of the LTE base station has low accuracy. A method of constructing a radio wave map of an LTE signal has been proposed as a method for overcoming the accuracy, but it takes a lot of time and cost to perform high-density collection in a wide area. In this paper, we describe a method of creating a high-density DB for the entire region by using vehicle-based partial collection data. To create a positioning database, we applied the idea of Generative Adversarial Network (GAN), which has recently been in the spotlight in the field of deep learning, and learned the collected data. Then, a virtually generated map which having the smallest error from the actual data is selected as the optimum DB. We verified the effectiveness of the positioning DB generation algorithm using the positioning data obtained from un-collected area.

• Journal of Magnetics
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• v.22 no.1
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• pp.150-154
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• 2017

A novel high precision spatial positioning method utilizing the electromagnetic momentum, i.e., Electromagnetic Momentum Positioning (EMP), is proposed in this paper. By measuring the momentum of the electromagnetic field around the small current loop, the relative position between the sensor and the current loop is calculated. This method is particularly suitable for the application of close-range and high-precision positioning, e.g., data gloves and medical devices in personal healthcare, etc. The simulation results show that EMP method can give a high accuracy with the positioning error less than 1 mm, which is better than the traditional magnetic positioning devices with the error greater than 1 cm. This method lays the foundation for the application of data gloves to meet the accurate positioning requirement, such as the high precision interaction in Virtual Reality (VR), Augmented Reality (AR) and personal wearable devices network.

• Journal of Platform Technology
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• v.11 no.5
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• pp.97-103
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• 2023

Unlike GPS, which is an outdoor positioning technology that is universally and uniformly used all over the world, various technologies are still being developed in the field of indoor positioning technology. In order to acquire accurate indoor location information, a standard of representative indoor positioning technology is required. Recently, indoor positioning technology is expanding into the Real Time Location Service (RTLS) area based on high-precision location data. Accordingly, a new type of indoor positioning technology is being proposed. Thanks to the development of artificial intelligence, artificial intelligence-based indoor positioning technology using wireless signal data of a smartphone is rapidly developing. At this time, in the process of collecting data necessary for artificial intelligence learning, data that is distorted or inappropriate for learning may be included, resulting in lower indoor positioning accuracy. In this study, we propose a data preprocessing technology for artificial intelligence learning to obtain improved indoor positioning results through the refinement process of the collected data.

• Journal of Positioning, Navigation, and Timing
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• v.13 no.4
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• pp.431-439
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• 2024

The International GNSS Service (IGS) provides real-time satellite orbit, clock, and bias corrections through its Real-Time Service (RTS). In addition, Galileo has offered the Galileo High Accuracy Service (HAS) since January 24, 2023, further improving positioning accuracy for GPS and Galileo satellites. HAS data are available through Galileo's E6 signal and Ntrip, enhancing positioning accuracy for GPS and Galileo satellites. This study compared Galileo HAS corrections data and IGS RTS corrections data obtained from the BKG Ntrip client (BNC) on February 1 and June 23, 2024. The orbit, clock, and bias corrections of each satellite were analyzed, revealing that HAS and IGS RTS correction values followed similar trends for most satellites. Additionally, satellite position and clock values computed from these corrections were compared with SP3 and CLK data for accuracy. To perform positioning using HAS, a Code-PPP algorithm was developed, and positioning accuracy was evaluated for GPS-only, Galileo-only, and multi-constellation modes using both GPS and Galileo. Tropospheric errors were mitigated using the Global Model of Pressure and Temperature (GPT) with the Global Mapping Function (GMF), and ionospheric corrections were applied using the Global Ionospheric Map (GIM). As a result, sub-meter level positioning accuracy was achieved. Among the correction types, IGS RTS corrections provided better accuracy for GPS-only measurements, while Galileo HAS corrections yielded superior accuracy for Galileo-only positioning.

• Journal of Astronomy and Space Sciences
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• v.28 no.3
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• pp.217-223
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• 2011

Precise point positioning (PPP) is increasingly used in several parts such as monitoring of crustal movement and maintaining an international terrestrial reference frame using global positioning system (GPS) measurements. An accuracy of PPP data processing has been increased due to the use of the more precise satellite orbit/clock products. In this study we developed PPP algorithm that utilizes data collected by a GPS receiver. The measurement error modelling including the tropospheric error and the tidal model in data processing was considered to improve the positioning accuracy. The extended Kalman filter has been also employed to estimate the state parameters such as positioning information and float ambiguities. For the verification, we compared our results to other of International GNSS Service analysis center. As a result, the mean errors of the estimated position on the East-West, North-South and Up-Down direction for the five days were 0.9 cm, 0.32 cm, and 1.14 cm in 95% confidence level.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• Proceedings of the KSR Conference
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• 2010.06a
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• pp.2002-2007
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• 2010

The objective of this study is the developement monitoring measurement using global positioning digital datalogger system for monitoring measurement of railway construction sites. For the replacement of current passive data communication, Global positioning digital datalogger system using active communication is studied for the first time with in a country. Therefore data communication method and analyzing program of automatic measurement data is developed for the global positioning automatic digital datalogger system. The results of this study will be using real time automatic monitoring measurement of railway structures.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.3
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• pp.92-101
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• 2010

Energy efficiency and safe mobility are the two key constituents of the future automobile. The technologies that enable these features are now heavily dependent upon information and communication technology rather than traditional auto-mechanical technology. This paper presents an exploratory project 'Smart&Green Vehicle Project' at Western Michigan University which is to improve the geographical location accuracy of vehicles and to study various applications of making such location data available. Global Positioning System (GPS), Inertial Navigation System (INS), Vehicular Ad-hoc Network (VANET) technology, and data fusion among these technologies are investigated. Testing and evaluation is done on systems which will gather vehicular positioning data during GPS signal loss. Vehicles in urban settings do not acquire accurate positioning data from GPS alone; therefore there is a need for exploration into technology that can assist GPS in urban settings. The goal of this project is to improve the accuracy of positioning data during a loss of GPS signal. Controlled experiments are performed to gather data which aided in assessing the feasibility of these technologies for use in vehicular platforms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.1
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• pp.69-85
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• 2019

BeiDou navigation satellite system (BDS) is one of the four main types of global navigation satellite systems. The current system has been widely used by the military and by the aerospace, transportation, and marine fields, among others. However, challenges still remain in the BeiDou system, which requires rapid responses for delay-sensitive devices. A differential positioning algorithm called the data center-based differential positioning (DCDP) method is widely used to avoid the influence of errors. In this method, the positioning information of multiple base stations is uploaded to the data center, and the positioning errors are calculated uniformly by the data center based on the minimum variance or a weighted average algorithm. However, the DCDP method has high delay and overload risk. To solve these problems, this paper introduces edge computing to relieve pressure on the data center. Instead of transmitting the positioning information to the data center, a novel method called edge computing-based differential positioning (ECDP) chooses the nearest reference station to perform edge computing and transmits the difference value to the mobile receiver directly. Simulation results and experiments demonstrate that the performance of the ECDP outperforms that of the DCDP method. The delay of the ECDP method is about 500ms less than that of the DCDP method. Moreover, in the range of allowable burst error, the median of the positioning accuracy of the ECDP method is 0.7923m while that of the DCDP method is 0.8028m.