• 한국재난정보학회 논문집
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• 제17권3호
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• pp.521-534
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• 2021

연구목적: 본 논문에서는 스마트 폰의 IMU센서를 사용한 PDR 방식의 실내 위치 추적 기법을 제안하고자 하며, 보다 정확한 추정을 위해 스마트 폰의 자세 변화로 인한 오류를 최소화하여 이동방향, 걸음 수, 보폭의 세 가지 정보를 추정하는 방법을 제안하고자 한다. 연구방법: 제안된 기법의 유효성과 성능을 실험을 통해 확인하고자 하였으며, 동일한 조건에서 기존 성능기법과 비교해 본 논문에서 제안하는 기법을 입증 하고자 한다. 연구결과: 실험을 통해 측정된 측위 오차는 기존 기법의 평균 오차가 1.84m이고, 제안된 기법의 평균 오차는 0.76m로서 제안된 기법이 기존 기법보다 보행자의 실제 이동 방향과 위치를 더욱 정확하게 추정할 수 있음을 확인하였다. 결론: 본 논문에서 제안하는 스마트 폰의 IMU센서를 사용한 PDR 방식의 실내 위치 추적 기법은 모든 국민이 보유한 스마트 폰을 활용하여 재난 시 신속한 대피를 위한 자신의 위치 인식 및 이동 방향 인식에 활용이 가능할 것으로 기대된다.

• 한국안전학회지
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• 제36권5호
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• pp.71-78
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• 2021

There are regularly planned overhaul periods in thermal power plants, which involve the maintenance of the boiler of the power plants. However, thermal power plants workers are always exposed to risk during overhaul periods owing to the narrow space and significant dust inside the boiler. Therefore, it is essential to develop a safety monitoring system that is suitable for operating in this type of environment. In this study, we developed not only a worker three-dimensional (3D)-location monitoring system that can monitor and record the entry/exit of workers, their 3D-location, and fall accidents but also a method to secure the working environment and operation efficiency. This system comprises of a worker tag, which was equipped with an inertial measurement unit, a barometric pressure sensor, and a Bluetooth low energy (BLE), and the tags were given to each worker. In addition, the location of workers inside the boiler was measured using a pedestrian dead reckoning (PDR) method and BLE beacons. The location data of the workers tag were transmitted to the integrated database (DB) server through a gateway, and to the administrator monitoring system. The performance of the system was demonstrated inside an actual thermal power plant boiler, and the accuracy and reliability of the system were verified through a number of repeated tests. These results provide insights on designing a new system for monitoring enclosed spaces.

• Journal of Positioning, Navigation, and Timing
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• 제12권4호
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• pp.399-408
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• 2023

In this paper, we propose a solution that enables continuous indoor/outdoor positioning of smartphone users through the integration of Pedestrian Dead Reckoning (PDR) and GPS/WiFi signals. Considering that accurate step detection affects the accuracy of PDR, we propose a Deep Neural Network (DNN)-based technology to distinguish between walking and non-walking signals such as walking in place. Furthermore, in order to integrate PDR with GPS and WiFi signals, a technique is used to select a proper measurement by distinguishing between indoor/outdoor environments based on GPS Dilution of Precision (DOP) information. In addition, we propose a technology to adaptively change the measurement error covariance matrix by detecting measurement outliers that mainly occur in the indoor/outdoor transition section through a residual-based χ² test. It is verified through experiments on a testbed that these technologies significantly improve the performance of PDR and PDR/GPS/WiFi fingerprinting-based integrated pedestrian navigation.

• 한국측량학회지
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• 제36권5호
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• pp.395-401
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• 2018

Recently, most of mobile devices are equipped with GNSS (Global Navigation Satellite System). When the GNSS signal is available, it is easy to obtain position information. However, GNSS is not suitable solution for indoor localization, since the signals are normally not reachable inside buildings. A wide varieties of technology have been developed as a solution for indoor localization such as Wi-Fi, beacons, and inertial sensor. With the increased sensor combinations in mobile devices, mobile devices also became feasible to provide a solution, which based on PDR (Pedestrian Dead Reckoning) method. In this study, we utilized the combination of three sensors equipped in mobile devices including accelerometer, digital compass, and gyroscope and applied three representative PDR methods. The proposed methods are done in three stages; step detection, step length estimation, and heading determination and the final indoor localization result was evaluated with terrestrial LiDAR (Light Detection And Ranging) data obtained in the same test site. By using terrestrial LiDAR data as reference ground truth for PDR in two differently designed experiments, the inaccuracy of PDR methods that could not be found by existing evaluation method could be revealed. The firstexperiment included extreme direction change and combined with similar pace size. Second experiment included smooth direction change and irregular step length. In using existing evaluation method which only checks traveled distance, The results of two experiments showed the mean percentage error of traveled distance estimation resulted from three different algorithms ranging from 0.028 % to 2.825% in the first experiment and 0.035% to 2.282% in second experiment, which makes it to be seen accurately estimated. However, by using the evaluation method utilizing terrestrial LiDAR data, the performance of PDR methods emerged to be inaccurate. In the firstexperiment, the RMSEs (Root Mean Square Errors) of x direction and y direction were 0.48 m and 0.41 m with combination of the best available algorithm. However, the RMSEs of x direction and y direction were 1.29 m and 3.13 m in the second experiment. The new evaluation result reveals that the PDR methods were not effective enough to find out exact pedestrian position information opposed to the result from existing evaluation method.

• 정보과학회 컴퓨팅의 실제 논문지
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• 제23권3호
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• pp.148-155
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• 2017

본 논문에서는 스마트폰 내 가속도, 자기장, 자이로스코프 센서들을 이용해 사용자의 걸음과 걸음 수를 인식하는 시스템을 개발하였다. 센서 데이터 분석을 통해 사용자의 걸음을 스마트폰을 손에 든 상황과 주머니에 넣은 상황에서의 걸음 패턴으로 분류하고 이를 추출할 수 있는 알고리즘을 사용하여 걸음 수 인식의 정확성을 개선하였다. 알고리즘을 적용한 결과 손에든 상황에서 96%, 주머니에 넣은 상황에서 95.5% 수준의 걸음 수 인식 정확도를 보였으며, 나머지 터치 스크린, 위아래 반복 흔들기, 앉아서 일어서기, 오른쪽 왼쪽 흔들기와 같은 행위로 인해 발생하는 6%의 오차를 확인하였다.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
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• pp.251-256
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• 2006

In the work package 'Integrated Positioning' of the research project NAVIO (Pedestrian Navigation Systems in Combined Indoor/Outdoor Environements) we are dealing with the navigation and guidance of visitors of our University. Thereby start points are public transport stops in the surroundings of the Vienna University of Technology and the user of the system should be guided to certain office rooms or persons. For the position determination of the user different location sensors are employed, i.e., for outdoor positioning GPS and dead reckoning sensors such as a digital compass and gyro for heading determination and accelerometers for the determination of the travelled distance as well as a barometric pressure sensor for altitude determination and for indoor areas location determination using WiFi fingerprinting. All sensors and positioning methods are combined and integrated using a Kalman filter approach. Then an optimal estimate of the current location of the user is obtained using the filter. To perform an adequate weighting of the sensors in the stochastic filter model, the sensor characteristics and their performance was investigated in several tests. The tests were performed in different environments either with free satellite visibility or in urban canyons as well as inside of buildings. The tests have shown that it is possible to determine the user's location continuously with the required precision and that the selected sensors provide a good performance and high reliability. Selected tests results and our approach will be presented in the paper.

• Journal of Positioning, Navigation, and Timing
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• 제8권2호
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• pp.49-58
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• 2019

A number of techniques have been studied to estimate the position of pedestrians in indoor space. Among them, the technique of estimating the position using only the sensors attached to the body of the pedestrian without using the infrastructure is regarded as a very important technology for special purpose pedestrians such as the firefighters. In particular, it forms a research field under the name of Pedestrian Dead Reckoning (PDR). In this paper, we focus on a method for step detection which is essential when performing PDR using Inertial Measurement Unit (IMU) mounted on a shoe. Many researches have been done to detect the stance phase where the foot contacts the ground. Most of these methods, however, have a way to detect the specific size of the sensor signal and require thresholds for these methods. This has the difficulty of changing these thresholds if the user is different. To solve this problem, we propose a stance phase detection method that does not require any threshold value. It is expected that this result will make it easier to commercialize the technology because PDR can be implemented without user-dependent parameter setting.

• 전자통신동향분석
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• 제29권6호
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• pp.113-125
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• 2014

최근 모바일 단말 기술의 발전과 무선망의 성능 향상에 따른 다양한 서비스가 제공되고 있는 추세이며, 위치정보인식시스템과 결합된 서비스에 많은 관심이 높아졌다. 본고에서는 GPS(Global Positioning System)의 신호가 미치지 못하는 건물의 실내환경에 적합한 경로 안내서비스 및 지하시설물 안내 등 초정밀 실내 측위 서비스를 제공하기 위한 융합 측위 방안을 제안한다. 융합 측위 방안은 실내외 연속 측위를 위해 실외에서는 GPS를 이용하고 실내환경에서는 WLAN 기반의 측위 전용 AP(Access Point)를 이용, 전파신호의 LoS(Line of Sight)를 확보하여 측위하고 전파음영지역에서는 스마트폰의 가속도, 자이로센서 등 여러 가지 관성센서를 활용하여 PDR(Pedestrian Dead Reckoning) 알고리즘 등을 적용하여 측위한다. 또한 측위 정확도 향상 및 오차를 줄이기 위한 방법으로 LSE(Least Squire Estimation) 및 EKF(Extended Kalman Filter), KNN(K-Neighbor Node)/MSSM(Maximum Signal Strength Model) Algorithm 등 다양한 융합 측위 알고리즘을 적용하여 실내환경에 적합한 최적의 초정밀 실내 측위 서비스를 제공한다.

• Journal of Positioning, Navigation, and Timing
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• 제9권3호
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• pp.169-173
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• 2020

Location-based services are used as core services in various fields. In particular, in the field of public services such as emergency rescue, accurate location estimation technology is very important. Recently, the technology of tracking the location of self-isolation subjects for COVID-19 has become a major issue. Therefore, location estimation technology using personal smart devices is being studied in various ways, and the most widely used method is to use GPS. Other representative methods are using Wi-Fi, Pedestrian Dead Reckoning (PDR), Bluetooth Low Energy (BLE) beacons, and LTE signals. In this paper, we introduced a positioning technology using deep learning based on LTE Channel State Information-Reference Signal (CSI-RS) data, and confirmed the possibility through an outdoor location estimation experiment using a commercial LTE signal.

• 제어로봇시스템학회논문지
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• 제18권11호
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• pp.1011-1017
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• 2012

Augmented Reality is being widely used not only for Smartphone users but also in industries such as maintenance, construction area. With smartphone, due to the low localization accuracy and the requirement of special infrastructure, current LBS (Localization Based Service) is limited to show P.O.I. (Point of Interest) nearby. Improvement of IMU (Inertial Measurement Unit) based deadreckoning is presented in this paper. Additional sensors such as the magnetic compass and magnetic flux sensors are used as well as the accelerometer and the gyro for getting more movement information. Based on the pedestrian movement, appropriate sensor information is selected and the complementary filter is used in order to enhance the accuracy of the localization. Additional sensors are used to measure the movements of the upper body and the head and to provide the user's line of sight.