• Journal of Advanced Marine Engineering and Technology
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• v.38 no.5
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• pp.566-572
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• 2014

Recently, indoor navigation has been applied in large convention centers by using wireless sensor networks (WSNs), which provide not only a user's path to be traveled but also orientation and shopping information to increase user's convenience. This paper presents the localization system for estimating relative coordinates without pre-deployment of the reference node based on ultra wide band (UWB) ranging system, which is relatively suitable for indoor localization compared to other wireless communications, and azimuth sensor. The proposed localization system which consists of an azimuth sensor and a mobile node composed of three nodes estimates relative coordinates of the reference node without applying any recursive and time consumption algorithms. Also, in the process of estimating relative coordinates of the reference node, ranging errors are minimized through the proposed technique and the number of nodes can be reduced. Experimental results show the feasibility and validity of the proposed system.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.113-120
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• 2017

In recent years, indoor localization has been researched for the improvement of its localization accuracy capability in Wi-Fi environment. The fingerprint and RF propagation models has been the main approach in determining indoor positioning. With the use of fingerprint, a low-cost, versatile localization system can be achieved without the use of external hardware. However, only a few research have been made on virtual access points (VAPs) among indoor localization models. In this paper, the idea of indoor localization system using fingerprint with the addition of VAP in Wi-Fi environment is discussed. The idea is to virtually add APs in the existing indoor Wi-Fi system, this would mean additional virtually APs in the network. The experiments of the proposed algorithm shows the positive results when 2VAPs are used compared with only APs. A combination of 3APs and 2VAPs in the 3rd case had the lowest average error of 3.99 among its 4 scenarios.

• Journal of information and communication convergence engineering
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• v.6 no.1
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• pp.10-14
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• 2008

The robot localization problem is a key problem in making truly autonomous robots. In this work we provide thorough discussions of Ultrasonic Positioning System can be applied to the localization problem. First, we look at the use of Kalman filters and basic concept and the equation involved in Kalman filters. Secondly, we create understanding of how the Kalman filters can be implemented in robot localization. We show our discussion and experiments how Kalman filters applied to the localization problem. Lastly, we perform simulations using Usat Wheel Chair robot in our own general Kalman filters robot monitoring software.

• The Journal of Korea Robotics Society
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• v.14 no.4
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• pp.293-300
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• 2019

In this paper, an Embedded solution for fast navigation and precise positioning of mobile robots by floor features is introduced. Most of navigation systems tend to require high-performance computing unit and high quality sensor data. They can produce high accuracy navigation systems but have limited application due to their high cost. The introduced navigation system is designed to be a low cost solution for a wide range of applications such as toys, mobile service robots and education. The key design idea of the system is a simple localization approach using line features of the floor and delayed localization strategy using topological map. It differs from typical navigation approaches which usually use Simultaneous Localization and Mapping (SLAM) technique with high latency localization. This navigation system is implemented on single board Raspberry Pi B+ computer which has 1.4 GHz processor and Redone mobile robot which has maximum speed of 1.1 m/s.

• Journal of Positioning, Navigation, and Timing
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• v.11 no.1
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• pp.35-44
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• 2022

In this paper, a dual foot (DF)-PDR system is proposed for the fusion of integration (IA)-based PDR systems independently applied on both shoes. The horizontal positions of the two shoes estimated from each PDR system are fused based on a particle filter. The proposed method bounds the position error even if the walking time increases without an additional sensor. The distribution of particles is a non-Gaussian distribution to express the lateral error due to systematic drift. Assuming that the shoe position is the pedestrian position, the multi-modal position distribution can be fused into one using the Gaussian sum. The fused pedestrian position is used as a measurement of each particle filter so that the position error is corrected. As a result, experimental results show that position of pedestrians can be effectively estimated by using only the inertial sensors attached to both shoes.

• Journal of Broadcast Engineering
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• v.23 no.1
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• pp.154-161
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• 2018

As the number of smartphone users increases, research on indoor location recognition service is necessary. Access to indoor locations is predominantly WiFi, Bluetooth, etc., but in most quarters, WiFi is equipped with WiFi functionality, which uses WiFi features to provide WiFi functionality. The study uses the random forest algorithm, which employs the fingerprint index of the acquired WiFi and the use of the multI-value classification method, which employs the receiver signal strength of the acquired WiFi. As the data of the fingerprint, a total of 4 radio maps using the Mac address together with the received signal strength were used. The experiment was conducted in a limited indoor space and compared to an indoor location recognition system using an existing random forest, similar to the method proposed in this study for experimental analysis. Experiments have shown that the system's positioning accuracy as suggested by this study is approximately 5.8 % higher than that of a conventional indoor location recognition system using a random forest, and that its location recognition speed is consistent and faster than that of a study.

• KIISE Transactions on Computing Practices
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• v.23 no.4
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• pp.210-216
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• 2017

In the paper we propose and implement a new indoor localization system where the techniques of magnetic field based fingerprinting and pedestrian dead reckoning are combined. First, we determine a target's location by comparing acquired magnetic field values with a magnetic field map containing pre-collected field values at different locations and choosing the location having the closest value. As the target moves, we use pedestrian dead reckoning to estimate the expected moving path, reducing the maximum positioning error of the initial location. The system eliminates the problem of localization error accumulation in pedestrian dead reckoning with the help of the fingerprinting and does not require Wi-Fi AP infrastructure, enabling cost-effective localization solution.

• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.29-35
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• 2021

In this paper, we propose a method that combines KNN(K-Nearest Neighbor), Local Map Classification and Bayes Filter as a way to increase the accuracy of location positioning. First, in this technique, Local Map Classification divides the actual map into several clusters, and then classifies the clusters by KNN. And posterior probability is calculated through the probability of each cluster acquired by Bayes Filter. With this posterior probability, the cluster where the robot is located is searched. For performance evaluation, the results of location positioning obtained by applying KNN, Local Map Classification, and Bayes Filter were analyzed. As a result of the analysis, it was confirmed that even if the RSSI signal changes, the location information is fixed to one cluster, and the accuracy of location positioning increases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.75-80
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• 2021

Positioning technology is performing important functions in augmented reality, smart factory, and autonomous driving. Among the positioning techniques, the positioning method using beacons has been considered a challenging task due to the deviation of the RSSI value. In this study, the position of a moving object is predicted by training a neural network that takes the RSSI value of the receiver as an input and the distance as the target value. To do this, the measured distance versus RSSI was collected. A neural network was introduced to create synthetic data from the collected actual data. Based on this neural network, the RSSI value versus distance was predicted. The real value of RSSI was obtained as a neural network for generating synthetic data, and based on this value, the coordinates of the object were estimated by learning a neural network that tracks the location of a terminal in a virtual environment.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2006.05a
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• pp.84-89
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• 2006

GPS(Global Positioning System)는 이미 전 세계적으로 다양한 응용분야를 가지고 널리 사용되고 있다. 그러나 현재 지구 주위를 운항하고 있는 24개의 GPS 위성으로 지상 어느 곳에서나 24시간 동안 위성신호를 수신할 수 있게 되었지만 고층 빌딩과 같은 도심 계곡, 복잡한 한국지형, 산악지역 등에서의 위성 장애물에 의한 신호의 차단으로 한계가 발생하고 있다. NAVSYNC의 CW25 GPS 수신기는 -156dBm이하의 신호강도에서도 확실한 위치 fix가 가능하고, 이러한 능력은 도심지나, 울창한 숲과 심지어는 건물 안과 같이 신호가 미약한 지역에서도 사용될 수 있다. 이에 본 연구에서는 Indoor GPS 수신기를 이용하여 측지분야에서 검증된 GPS측량방법을 통해 수신 데이터를 비교 분석하고 수신기의 정밀도에 관한 연구를 하고자 한다.