• Journal of Advanced Marine Engineering and Technology
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• v.40 no.1
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• pp.62-68
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• 2016

The fingerprint, which is one of the methods of indoor localization using WiFi, has been frequently studied because of its ability to be implemented via wireless access points. This method has low positioning resolution and high computational complexity compared to other methods, caused by its dependence of reference points in the radio map. In order to compensate for these problems, this paper presents a radio map designed algorithm based on the log-distance path loss model fusing a WiFi and BLE fingerprint. The proposed algorithm designs a radio map with variable values using the log-distance path loss model and reduces distance errors using a median filter. The experimental results of the proposed algorithm, compared with existing fingerprinting methods, show that the accuracy of positioning improved by from 2.747 m to 2.112 m, and the computational complexity reduced by a minimum of 33% according to the access points.

• Journal of KIISE
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• v.44 no.6
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• pp.643-648
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• 2017

Among Wi-Fi based indoor positioning systems, fingerprinting localization is the most common technique with high precision. However, construction of the initial radio map and the update process require considerable labor and time effort. To address this problem, we propose an efficient method that constructs the initial radio map at each vertex based on a graph. In addition, we introduce a method to update the radio map automatically by mapping signal data acquired from users to the reference point created on each edge. Since the proposed method collects signal data manually only at the vertex of the graph to build the initial radio map and updates it automatically, our proposed method can dramatically reduce labor and time effort, which are the disadvantages of the conventional fingerprinting method. In our experimental study, we show validity of our radio map update method by comparing with the actual reference point data. We also show that our proposed method is able to construct the radio map with an accuracy of about 3.5m by automatically updating the radio map.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.11
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• pp.2411-2418
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• 2011

The most typically utilized positioning method in the existing indoor WPS that is a positioning method utilizing distributed wireless network is the finger print. Its positioning is carried out by calculating the difference between the AP information map and WiFi AP signal collected. However, there are problems like low accuracy and high cost when the existing method and the radio map formation are applied to outdoors. In this paper, the characteristics of the existing WPS are surveyed and their problems are examined. In addition, we propose a new WPS using weak signal and a method to construct radio map in order to solve the above problems. And then, the results of experimental test will be analyzed.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.59-61
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• 2022

Among indoor positioning system techniques using wireless APs, fingerprint techniques collect Mac information and reception strength of APs before performing positioning, build a radio map, and compare it with AP information collected during positioning. However, the existing Radio Map construction method has a problem in that signal interference occurs due to collisions of numerous APs depending on the indoor environment, and the signal strength search result is not always constant. Therefore, this paper compares the existing fixed radio map construction method and the variable radio map construction technique that actively analyzes and constructs the measurement area itself according to signal strength.

• Spatial Information Research
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• v.23 no.3
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• pp.1-10
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• 2015

Due to recent improvement of various sensor technologies, indoor positioning becomes available. However, Indoor positioning technologies by Wi-Fi radio map and acceleration sensor and digital campus still have a certain level of errors and a number of researches have been done to increase the positioning accuracy of the indoor positioning. If we could provide a room level accuracy, indoor location based services with current indoor positioning methods such as Wi-Fi radio map and acceleration sensors would be possible. In this paper, we propose an indoor map matching method to provide a room level accuracy based on hidden markov model.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.47-49
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• 2022

IPS is an indoor positioning system that replaces GPS, which is less accurate due to walls and roofs inside buildings. Radio signals such as Wi-Fi are used instead of satellite signals, and complex methods are being used to increase accuracy. In this paper, we present a more efficient positioning method than before by applying high-level signals to high-accuracy fingerprint techniques. A radio map is configured with high-level signals collected by utilizing Wi-Fi APs, which are more than in the past. The suitability of use was confirmed by analyzing the pattern represented by the configured radio map for each location.

• Journal of Advanced Marine Engineering and Technology
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• v.40 no.9
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• pp.836-841
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• 2016

An indoor positioning system using Wi-Fi is essential to produce a radio map that combines the indoor space of two or more dimensions, the information of node positions, and etc. in processing for constructing the radio map, the measurement of the received signal strength indicator(RSSI) and the confirmation of node placement information counsume substantial time. Especially, when the installed wireless environment is changed or a new space is created, easy installation of the node and fast indoor radio mapping are needed to provide indoor location-based services. In this paper, to reduce the time consumption, we propose an algorithm to distinguish the straight and curve lines of a corridor section by RSSI visualization and Sobel filter-based edge detection that enable accurate node deployment and space analysis using cable-type Wi-Fi node installed at a 3 m interval. Because the cable type Wi-Fi is connected by a same power line, it has an advantage that the installation order of nodes at regular intervals could be confirmed accurately. To be able to analyze specific sections in space based on this advantage, the distribution of the signal was confirmed and analyzed by Sobel filter based edge detection and total RSSI distribution(TRD) computation through a visualization process based on the measured RSSI. As a result to compare the raw data with the performance of the proposed algorithm, the signal intensity of proposed algorithm is improved by 13.73 % in the curve section. Besides, the characteristics of the straight and the curve line were enhanced as the signal intensity of the straight line decreased by an average of 34.16 %.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2657-2675
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• 2013

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.10
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• pp.1994-2000
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• 2016

Technology that can determine the location of individuals is required in a variety of applications such as location based control, a personalized advertising. Missing-child prevention and support for field trips, and applications such as push events based on the user's location is endless. In particular, the technology that can determine the location without interruption in the indoor and outdoor spaces have been studied a lot recently. Because emphasizing on accuracy of the positioning, many conventional research have constraints such as using of additional sensing devices or special mounting devices. The algorithm proposed in this paper has the purpose of performing the positioning only with standard equipment of the smart phone that has the most users. In this paper, sensor Fusion with GPS, WiFi Radio Map, Accelerometer sensor and Particle Filter algorithm is designed and implemented. Experimental results of this algorithm shows superior performance than the other compared algorithm. This could confirm the possibility of using proposed algorithm on actual environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.6
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• pp.531-539
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• 2013

Fingerprinting technique uses the radio signal strength measured reference locations is typically used, although there are many Wi-Fi based location tracking techniques. However, it needs numerous reference locations for precision and accuracy. This paper the analyzes problems of previous techniques and proposes a fingerprinting system using reliability based on a signal strength map. The system collects the signal strength data from a number of reference locations designated by the developer. And then it generates path-loss models to one of the access points for each reference location. These models calculate the predicted signal strength and reliability for a lattice. To evaluate proposed method and system performance, We perform experiments in a $20m{\times}22m$ real indoor environment installed access points. According to the result, the proposed system reduced distance error than RADAR. Comparing the existing system, it reduced about 1.74m.