• Journal of Internet of Things and Convergence
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• v.10 no.4
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• pp.101-108
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• 2024

Indoor localization is a critical component for numerous applications, ranging from navigation in large buildings to emergency response. This paper presents an enhanced Pedestrian Dead Reckoning (PDR) scheme using smartphone sensors, integrating neural network-aided motion recognition, Kalman filter-based error correction, and multi-sensor data fusion. The proposed system leverages data from the accelerometer, magnetometer, gyroscope, and barometer to accurately estimate a user's position and orientation. A neural network processes sensor data to classify motion modes and provide real-time adjustments to stride length and heading calculations. The Kalman filter further refines these estimates, reducing cumulative errors and drift. Experimental results, collected using a smartphone across various floors of University, demonstrate the scheme's ability to accurately track vertical movements and changes in heading direction. Comparative analyses show that the proposed CNN-LSTM model outperforms conventional CNN and Deep CNN models in angle prediction. Additionally, the integration of barometric pressure data enables precise floor level detection, enhancing the system's robustness in multi-story environments. Proposed comprehensive approach significantly improves the accuracy and reliability of indoor localization, making it viable for real-world applications.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1917-1922
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• 2017

WPS(Wifi Positioning System) conducts positioning using wireless signals scattered in real world. This process is divided into two stages: Construction Stage that collects information on wireless signals for determining location and constructs a radio map and Positioning Stage that compares the constructed information with the collected information on wireless signals. WPS lowers the accuracy of positioning if changes occur to the collected signals during positioning. PDR have recently been studied. IPS is a system designed to find out the final destination by analyzing pedestrian's no. of gait, travel range, and direction through inertial sensors. If the positioning results of WPS appear in more than two locations, it can be thought as the problem of positioning accuracy. In some cases, problems occur. In this respect, this study analyzes the situations in which the problem as mentioned above occurs and proposes a system to solve this problem through PDR.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.979-982
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• 2013

위치정보를 이용한 서비스가 요구가 증가함에 따라, 실외를 중심으로 이뤄지던 위치정보서비스는 실내를 중심으로 요구되고 있다. 더불어 좀더 정확한 실내측위 시스템에 대한 필요성도 증가되고 있다. 최근의 위치 정보 서비스는 스마트폰의 보급과 함께 모바일 기기의 환경이 이용되고 있다. 본 논문은 스마트 디바이스 기반으로 WLAN(Wireless Local Area Network)과 DATABASE 를 이용한 Fingerprinting 방식을 제안한다. 또한 기존의 Fingerprinting 방식에 스마트 디바이스에 내장된 Gyroscope sensor 를 이용하여 모바일 환경에서 일부 영역에서 발생할 수 있는 신호의 오차를 보정하는 새로운 방법도 제시한다. 실제 테스트 환경을 구축하여 실험한 결과도 제시하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.6
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• pp.1123-1128
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• 2016

Recently BLE beacon has been widely used as a method for measuring the indoor location in the IoT Technique. But it requires a filtering technique for the measurement of the correct position. It is used the most fixed beacon. It is not accurate that calculates the position information through the identification of the beacon signal. Therefore, filtering is important. So it takes a lot of time, position measurement and filtering. Thus, we is to measure the exact position at the indoor using a mobile beacon. The measured beacon signal is composed of an ontology for reuse in the same pattern. RSSI is measured the receiver is the distance of the beacon. And this value configure the location ontology to be normalized by the relationship analysis between the values. The ontology is a method for calculating the position information of the moving beacon. It can detect fast and accurate indoor position information and provide the service.

• Journal of Korean Society for Geospatial Information Science
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• v.17 no.1
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• pp.157-166
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• 2009

There is a growing interest in ubiquitous-related research and applications. Among them, GPS-based LBS have been developed and used actively. Recently, with the increase of large size buildings and disastrous events, indoor spaces are getting attention and related research activities are being carried out. Core technologies regarding indoor applications may include 3D indoor data modeling and localization sensor techniques that can integrate with indoor data. However, these technologies have not been standardized and established enough to be applied to indoor implementation. Thus, in this paper, we propose a method to build a relatively simple 3D indoor data modeling technique that can be applied to indoor location based applications. The proposed model takes the form of 2D-based multi-layered structure and has capability for 2D and 3D visualization. We tested three prototype applications using the proposed model; CA(cellular automata)-based 3D evacuation simulation, network-based routing, and indoor moving objects tracking using a stereo camera.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.270-271
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• 2015

Recently, There is Increasing interest in the intelligent services using the Internet of Things indoor positioning technologies that enable the communication of information between the objects. In particular Applications and demand for the indoor location based services using smart devices has made active. An indoor location positioning technology for this purpose BLE (Bluetooth Low Energy) has been a lot of interest in technology increases. If iBeacon of BLE(Bluetooth Low Energy) is made available to provide a signal for the indoor location information measurement then reliability of Indoor location information is lowered by signal interference. In this paper, Proposes a technique for data acquisition method for obtaining reliable position information and reliable position information calculation method from signal information data of iBeacon.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.17 no.1
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• pp.25-30
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• 2024

Smart City, a key application area of the AIoT (Artificial Intelligence of Things), provides various services in safety, security, and healthcare sectors through location tracking and location-based services. an IPS (Indoor Positioning System) is required to implement location-based services, and wireless communication technologies such as WiFi, UWB (Ultra-wideband), and BLE (Bluetooth Low Energy) are being applied. BLE, which enables data transmission and reception with low power consumption, can be applied to various IoT devices such as sensors and beacons at a low cost, making it one of the most suitable wireless communication technologies for indoor positioning. BLE utilizes the RSSI (Received Signal Strength Indicator) to estimate the distance, but due to the influence of multipath fading, which causes variations in signal strength, it results in an error of several meters. In this paper, we conducted research on a path loss model that can be applied to BLE IPS for proximity services, and confirmed that optimizing the free space propagation loss coefficient can reduce the distance error between the Tx and Rx devices.

• 전기의세계
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• v.58 no.11
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• pp.36-42
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• 2009

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

• Korea Information Processing Society Review
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• v.16 no.3
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• pp.35-41
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• 2009