• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2022.10a
• /
• pp.615-616
• /
• 2022

Indoor positioning technology is being used in various application services due to the spread of smart devices. In addition to precisely positioning the location, simultaneous positioning of multiple users has been raised as a new problem. In addition, providing Internet services simultaneously with location measurement is essential for use in various applications. This paper aims to solve both of these problems by designing and implementing an indoor positioning system with the IEEE 802.11az standard

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.9
• /
• pp.898-902
• /
• 2015

Alternative radio-navigation technologies aim at providing continuous navigation solution even if one cannot use GNSS (Global Navigation Satellite System). In shadowing region such as indoor environment, GNSS signal is no longer available and the alternative navigation system should be used together with GNSS to provide seamless positioning. For soldiers in battlefield where GNSS signal is jammed or in street battle, the alternative navigation system should work without positioning infrastructure. Moreover, the radio-navigation system should have scalability as well as high accuracy performance. This paper presents a TWR (Two-Way-Ranging)-based cooperative positioning system (CPS) that does not require location infrastructure. It is assumed that some members of CPS can obtain GNSS-based position and they are called mobile anchors. Other members unable to receive GNSS signal compute their position using TWR measurements with mobile anchors and neighboring members. Error propagation in CPS is analytically studied in this paper. Error budget for TWR measurements is modeled first. Next, location error propagation in CPS is derived in terms of range errors. To represent the location error propagation in the CPS, Location Error Propagation Indicator (LEPI) is proposed in this paper. Simulation results show that location error of tags in CPS is mainly influenced by the number of hops from anchors to the tag to be positioned as well as the network geometry of CPS.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2017.10a
• /
• pp.313-315
• /
• 2017

Recently, application fields using machine learning have been widely expanded. In addition to the spread of smart devices, application services using location-based services are also in demand. However, it is difficult to provide the application service through the positioning in the indoor environment such as the specific space where the disaster situation where the information for positioning can not be collected and the actual location location information can not be used. In this situation, using the spatial information composed of the marker information and the markers of the neighbor registered in the augmented reality environment, positioning at a specific situation or position becomes possible. At this time, it is possible to learn the operation that makes the configuration of the marker-based spatial information correspond to the actual position through the machine learning, and the optimal positioning result can be obtained by minimizing the error. In this paper, we study the positioning methods required in specific situations using machine learning for learning of augmented reality markers and spatial information.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.3
• /
• pp.117-123
• /
• 2022

Unlike the satellite-based outdoor positioning system, the indoor positioning system utilizes various wireless technologies such as BLE, Wi-Fi, and UWB. BLE-based beacon technology can measure the user's location by periodically broadcasting predefined device ID and location information and using RSSI from the receiving device. Existing BLE-based indoor positioning system studies have many studies comparing the error between the user's actual location and the estimated location at a single point. In this paper, we propose a technique to evaluate the positioning accuracy according to the movement path or area by applying the entropy analysis model. In addition, simulation results show that calculated entropy results for different paths can be compared to assess which path is more accurate.

• 한국ITS학회:학술대회논문집
• /
• v.2007 no.10
• /
• pp.186-191
• /
• 2007

• Journal of the Society of Naval Architects of Korea
• /
• v.47 no.6
• /
• pp.836-842
• /
• 2010

Safety system including location monitoring system for blasting workers was studied. Positioning performance of the location monitoring system was highly dependent on communication protocol and the number of access points in the blasting cell. RTLS(Real Time Location System) is an important technology to develop the location information of workers and variously used to enhance workers safety. Location monitoring system with Cell-ID and RSSI wireless communication technology was verified to have a proper positioning performance for the steel block application.

• Journal of Communications and Networks
• /
• v.18 no.5
• /
• pp.837-845
• /
• 2016

Providing a customer with tailored location-based services (LBSs) is a fundamental problem. For location-estimation techniques with radio-based measurements, LBS applications are widely available for mobile devices (MDs), such as smartphones, enabling users to run multi-task applications. LBS information not only enables obtaining the current location of an MD but also provides real-time push-pull communication service. For indoor environments, localization technologies based on radio frequency (RF) pattern-matching approaches are accurate and commonly used. However, to survey radio information for pattern-matching approaches, a considerable amount of time and work is spent in indoor environments. Consequently, in order to reduce the system-deployment cost and computing complexity, this article proposes an indoor positioning approach, which involves using Asus Xtion to facilitate capturing RF signals during an offline site survey. The depth information obtained using Asus Xtion is utilized to estimate the locations and predict the received signal strength (RF information) at uncertain locations. The proposed approach effectively reduces not only the time and work costs but also the computing complexity involved in determining the orientation and RF during the online positioning phase by estimating the user's location by using a smartphone. The experimental results demonstrated that more than 78% of time was saved, and the number of samples acquired using the proposed method during the offline phase was twice as much as that acquired using the conventional method. For the online phase, the location estimates have error distances of less than 2.67 m. Therefore, the proposed approach is beneficial for use in various LBS applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2015.10a
• /
• pp.278-279
• /
• 2015

Recently, With the development of smart devices of interest in the intelligent indoor positioning service with a smart terminal it is increasing. The BLE(Bluetooth Low Energy) is an indoor location positioning technology has attracted the attention. However, there is no way to check the reliability of the position information collected by the smart terminal receiving a big location data. This is because the reliability of an indoor position information because of a signal interference to drop. In this paper, we propose a method of managing big data collected in order to obtain reliable position information from the position information of iBeacon.

• Journal of Advanced Navigation Technology
• /
• v.19 no.6
• /
• pp.564-573
• /
• 2015

GPS is the representative positioning technology for providing the location information. This technique has the disadvantage that does not operate in the shadow areas, such as urban or dense forest and the interior. This paper proposes a hybrid indoor positioning algorithm, which estimates a more accurate location of the terminal using strength of the Wi-Fi signal from the indoor AP. To determine the location of the user, we establish the most appropriate path loss model for the measurement environment. by using the RSSI value measured in a variety of environment such as building structure, person, distance, etc. The path loss exponent obtained by the path loss model is changed according to the environment. REKF, PF estimate the position of the terminal by using measured value from the AP with path loss exponent. For more accurate position estimation, we select positioning system by the value of threshold measured by experiments rather than a single positioning system. Experimental results using the proposed hybrid algorithm show that the performance is improved by about 17% than the conventional single positioning method.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.12 no.1
• /
• pp.19-25
• /
• 2017

Recently, indoor LBS has been attracting much attention because of its promising prospect. One of key technologies for its success is indoor location estimation. A popular one for indoor positioning is to find the location based on the strength of received Wi-Fi signals. Since the Wi-Fi services are currently prevalent, it can perform indoor positioning without any further infrastructure. However, it is found that its accuracy depends heavily on the surrounding radio environment. To alleviate this difficulty, we present a novel indoor position technique employing the geomagnetic characteristics as well as Wi-Fi signals. The geomagnetic characteristic is known to vary according to the location. Therefore, employing the geomagnetic signal in addition to Wi-Fi signals is expected to improve the location estimation accuracy.