• Journal of information and communication convergence engineering
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• v.6 no.4
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• pp.459-465
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• 2008

Locations of nodes as well as gathered data from nodes are very important because generally multiple nodes are deployed randomly and data are gathered in wireless sensor network. Since the nodes composing wireless sensor network are low cost and low performance devices, it is very difficult to add specially designed devices for positioning into the nodes. Therefore in wireless sensor network, technology positioning nodes precisely using low cost is very important and valuable. This research proposes Cooperative Positioning System, which raises accuracy of location positioning and also can find positions on multiple sensors within limited times.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.10
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• pp.1744-1751
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• 2008

This paper presents a monitor positioning algorithm to identify the power quality event source in the distribution system with distributed generations. This algorithm determines the appropriate number of monitors and their locations considering power system topology together with distributed generation. This paper summarizes the guidelines of monitor positioning into five principles and defines the weighting factors according to the principles. To evaluate the adequacy of monitor positioning results, ambiguity indices considering monitor location and system topology are proposed. The optimal number and locations of monitors are determined from optimization routine using the weighting factors and the monitor positioning results are evaluated in terms of ambiguity indices. The algorithm is applied to IEEE 13 bus test feeder and suggests the optimal number and locations of power quality monitors. The proposed approach can realize the expert's knowledge on monitor positioning into a sophisticated automatic computing algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.151-156
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• 2015

As the proliferation of smart phone, almost every user has one's own smart phone, and the user could get the global position and location based services using GPS system outdoors. But indoor positioning system using GPS does not work, and it could not detect global position using TDOA local positioning system. In this paper, a new indoor global positioning system for smart phone employing GPS receiver and electronic compass device is proposed with the TDOA local positioning system using acoustic signal, and the performance and the experimental result are described.

• ETRI Journal
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• v.40 no.1
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• pp.101-110
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• 2018

In wireless positioning systems using range measurements non-line-of-sight (NLOS) links cause estimation errors. Several studies have attempted to improve the positioning performance by mitigating these NLOS errors. These studies, however, have focused on the performance of a dataset consisting of three or more links. Therefore, measurement errors induced by links are averaged, and a reliable link is not fully utilized in the dataset. This paper proposes a Link Reliability based on Range Measurement (LRRM) scheme, which specifies the relative reliability of each link using residuals. The link reliability becomes the input to a Link Residual Weighting (LRW) scheme, which is also proposed as a weighted positioning scheme. Moreover, LRRM and LRW constitute new turbo positioning, where the estimation errors are reduced considerably by iterative updates.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.8
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• pp.742-747
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• 2004

Outdoors we can easily acquire our accurate location by GPS. However, the GPS signal can't be acquired indoors because of its weak signal power level. Adequate positioning method is demanded for many indoor positioning applications. At present, wireless local area network (WLAN) is widely installed in various areas such as airport, campus, and park. This paper proposes a positioning algorithm using WLAN signal strength to provide the position of the WLAN user indoors. There are two methods for WLAN based positioning, the signal propagation method uses signal strength model over space and the empirical method uses RF power propagation database. The proposed method uses the probability distribution of the power propagation and the maximum likelihood estimation (MLE) algorithm based on power strength DB. Test results show that the proposed method can provide reasonably accurate position information.

• ETRI Journal
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• v.41 no.4
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• pp.506-514
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• 2019

Currently, studies on autonomous driving are being actively conducted. Vehicle positioning techniques are very important in the autonomous driving area. Currently, the global positioning system (GPS) is the most widely used technology for vehicle positioning. Although technologies such as the inertial navigation system and vision are used in combination with GPS to enhance precision, there is a limitation in measuring the lane and position in shaded areas of GPS, like tunnels. To solve such problems, this paper presents the use of LED lighting for position estimation in GPS shadow areas. This paper presents simulations in the environment of three-lane tunnels with LEDs of different color temperatures, and the results show that position estimation is possible by the analyzing chromaticity of LED lights. To improve the precision of positioning, a fuzzy logic system is added to the location function in the literature [1]. The experimental results showed that the average error was 0.0619 cm, and verify that the performance of developed position estimation system is viable compared with previous works.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.49-54
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• 2021

Indoor positioning system becomes of increasing interests due to the demands for accurate indoor location information where Global Navigation Satellite System signal does not approach. Wi-Fi access points (APs) built in many construction in advance helps developing a Wi-Fi Received Signal Strength Indicator (RSSI) based indoor localization. This localization method first collects pairs of position and RSSI measurement set, which is called fingerprint database, and then estimates a user's position when given a query measurement set by comparing the fingerprint database. The challenge arises from nonlinearity and noise on Wi-Fi RSSI measurements and complexity of handling a large amount of the fingerprint data. In this paper, machine learning techniques have been applied to implement Wi-Fi based localization. However, most of existing indoor localizations focus on single position estimation. The main contribution of this paper is to develop multi-target localization by using deep neural, which is beneficial when a massive crowd requests positioning service. This paper evaluates the proposed multilocalization based on deep learning from a multi-story building, and analyses its learning effect as increasing number of target positions.

• Journal of IKEEE
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• v.26 no.2
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• pp.186-195
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• 2022

In this paper, we propose a new algorithm to improve the accuracy of indoor positioning techniques using Wi-Fi access points as beacon nodes. The proposed algorithm is based on the Weighted Centroid algorithm, a popular method widely used for indoor positioning, however, it improves some disadvantages of the Weighted Centroid method and also for other kinds of indoor positioning methods, by using the received signal strength correction method and genetic algorithm to prevent the signal strength fluctuation phenomenon, which is caused by the complex propagation environment. To validate the performance of the proposed algorithm, we conducted experiments in a complex indoor environment, and collect a list of Wi-Fi signal strength data from several access points around the standing user location. By utilizing this kind of algorithm, we can obtain a high accuracy positioning system, which can be used in any building environment with an available Wi-Fi access point setup as a beacon node.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.2
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• pp.141-148
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• 2023

In this paper, impacts of tropospheric delay gradient correction on PPP positioning performance were analyzed. A correction for tropospheric delay error due to the gradient was created and applied using external data, and reference station data were collected on a sunny day and a rainy day to analyze the GPS only dual-frequency PPP positioning results. As a result, on the sunny day, the convergence time was about 35 minutes and the final 3D position error was 10 cm, regardless of whether the correction for the tropospheric delay error by the gradient was applied. On the other hand, on the rainy day, the 3D position error converges only when the correction was applied, and the convergence time was about 34 minutes. Furthermore, the final 3D position error was improved from 30 cm to 10 cm. In addition, the analysis of the PPP by reference station location on the rainy day showed that the PPP positioning performance was improved when the correction was applied to a user located in an area where the weather changes.

• Journal of the Semiconductor & Display Technology
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• v.23 no.1
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• pp.71-78
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• 2024

In indoor environments, since global positioning system (GPS) signals can be blocked by obstacles, such as building structure. the performance of GPS-based positioning methods can be degraded because of the loss of GPS signals. To solve this problem, various localization schemes using inertial measurement unit (IMU) sensors, such as gyroscope, accelerometer, and magnetometer, have been proposed to enhance the positioning accuracy in indoor environments. IMU-based positioning methods can estimate the location of the user by calculating the velocity and heading angle of the user without the help of GPS. However, low-cost MEMS IMUs may lead to drift error and large bias. In addition, positioning errors in IMU-based positioning approaches can be caused by the irrelevant motion of the pedestrian. In this study, we propose an enhanced indoor positioning method that provides more reliable localization results by using the camera, light detection and right (LiDAR), and ARKit framework on the iPhone. Through reliable positioning results and augmented reality (AR) experiences, our indoor positioning system can provide indoor space guidance services.