• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1779-1788
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• 2012

Recently, demands of application services in consideration of interior environment according to the stream of times, Ubiquitous. In case of interior location-based service, WLAN is now mostly used. But it is largely affected by environmental changes. To solve this problem, lots of studies on UWB are underway. The reason why studies on UWB are much made lies in that it is not much affected by environment changes owing to radio wave characteristics. So this study suggests the location correction algorithm which derives values with less influence of environment and high accuracy and corrects with more accurate location information using Ubisense system based on UWB technologies. The location correction algorithm suggested is one made after constructing environment database and use it to estimate more accurate location from the location measuring system in a high position.

• Journal of Korea Multimedia Society
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• v.8 no.6
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• pp.761-767
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• 2005

The personal identification procedure through the fingerprints is divided as the classification process by the type of the fingerprints and the matching process to confirm oneself. Many existing researches for the classification and the matching of the fingerprint depend on the number of the minutiae of the fingerprints and the flow patterns by their direction information. In this paper, we focus on extracting the singular points by using the flow patterns of the direction information from identification. The extracted singular points are utilized as a standard point for the matching process by connecting with the extracted information from the singular point embodied. The orthogonal coordinates which is generated by the axises of the standard point can increase the accuracy of the fingerprints matching because of minimizing the effects on the location changes of the fingerprint images.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.168-171
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• 2016

In recent years, indoor localization in Wi-Fi environment has been researched for its location determining capability. 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 had the lowest average error in all 4 scenarios with 3.99 meters.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.641-642
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• 2018

Currently, the performance of positioning technology based on radio wave fingerprint is greatly influenced by the selection of data comparison algorithm. In this case, the accuracy of the indoor positioning can be greatly improved by the data expansion technique necessary for the learning structure. In this paper, we discuss the importance of learning structure that can be applied to actual positioning through classification and extension of learning data to construct learning structure based on Wi-Fi radio fingerprint.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.1122-1140
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• 2024

The importance of indoor positioning has grown in numerous application areas such as emergency response, logistics, and industrial automation. In ships, indoor positioning is also needed to provide services to passengers on board. Due to the complex structure and dynamic nature of ship environments, conventional positioning techniques have limitations in providing accurate positions. Compared to other indoor positioning technologies, Bluetooth 5.1-based indoor positioning technology is highly suitable for ship environments. Bluetooth 5.1 attains centimeter-level positioning accuracy by collecting In-phase and Quadrature (IQ) samples from wireless signals. However, distorted IQ samples can lead to significant errors in the final estimated position. Therefore, we propose an indoor positioning method for ships that utilizes a Deep Neural Network (DNN) combined with IQ fingerprint maps to overcome the challenges associated with accurate location detection within the ship. The results indicate that the accuracy of our proposed method can reach up to 97.76%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.8
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• pp.1934-1942
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• 2015

An estimation problem in the environment which GPS signals do not reach, should be solved by employing an indoor location estimation scheme. Location estimation schemes for indoor environments generally include the AOA, TOA, RSS, Fingerprint, and TDOA. For a ship environment where there exist many spaces enclosed by iron plates, the TDOA scheme is appropriate because location estimation is usually performed at a closed range. In this paper, we address the problem of estimating the location of a terminal under the ship environment. The problem of location estimation by using the TDOA is presented in detail, and then an algorithm for applying the estimation to the ship environment is proposed. Finally, the proposed algorithm of location estimation in a ship by the TDOA scheme is verified through simulations from three viewpoints.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.63-74
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• 2017

Recently in the area of ICT, the M2M and IoT are in the spotlight as a cutting edge technology with the help of advancement of internet. Among those fields, the smart home is the closest area to our daily lives. Smart home has the purpose to lead a user more convenient living in the house with WLAN (Wireless Local Area Network) or other short-range communication environments using automated appliances. With an arrival of the age of IoT, this can be described as one axis of a variety of applications as for the M2H (Machine to Home) field in M2M. In this paper, we propose a novel technique for estimating the location of a terminal that freely move within a specified area using the RSSI (Received Signal Strength Indication) in the WLAN environment. In order to perform the location estimation, the Fingerprint and KNN methods are utilized and the LMS with the gradient descent method and the proposed algorithm are also used through the error correction functions for locating the real-time position of a moving user who is keeping a smart terminal. From the estimated location, the nearest fixed devices which are general electric appliances were supposed to work appropriately for self-operating of virtual smart home. Through the experiments, connection and operation success rate, and the performance results are analyzed, presenting the verification results.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.1
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• pp.55-66
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• 2021

In this paper, we propose a signal propagation modeling technique for generating a positioning fingerprint DB based on Long Term Evolution (LTE) signals. When a DB is created based on the location-based signal information collected in an urban area, gaps in the DB due to uncollected areas occur. The spatial interpolation method for filling the gaps has limitations. In addition, the existing gap filling technique through signal propagation modeling does not reflect the signal attenuation characteristics according to directions occurring in urban areas by considering only the signal attenuation characteristics according to distance. To solve this problem, this paper proposes a Deep Neural Network (DNN)-based signal propagation functionalization technique that considers distance and direction together. To verify the performance of this technique, an experiment was conducted in Seocho-gu, Seoul. Based on the acquired signals, signal propagation characteristics were modeled for each method, and Root Mean Squared Errors (RMSE) was calculated using the verification data to perform comparative analysis. As a result, it was shown that the proposed technique is improved by about 4.284 dBm compared to the existing signal propagation model. Through this, it can be confirmed that the DNN-based signal propagation model proposed in this paper is excellent in performance, and it is expected that the positioning performance will be improved based on the fingerprint DB generated through it.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.8
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• pp.760-766
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• 2008

This paper proposes an efficient location technique to find an indoor location under the IEEE 802.11 wireless local area networks. The proposed method is based on the range measurements obtained from a simple radio frequency propagation model. Thus, unlike the radio frequency fingerprint correlation method, it does not suffer from the computational burden during the real-time location service period and can quickly reply the location requests of many users at the same time. To increase the location accuracy in spite of the frequent non-line-of-sight error occurrences, the proposed method calibrates the distortion of the non-line-of-sight error by a simple measurement surveying procedure that does not require the surveyor's manual interaction. Experimental results show the capability of the proposed method.

• Journal of Internet of Things and Convergence
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• v.2 no.4
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• pp.35-39
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• 2016

This paper is about IoT(Internet of Things)-based indoor localization scheme. GPS and WiFi are widely used to estimate the location of things. However, GPS has drawback of poor reception and radio disturbance in doors. To estimate the location in WiFi-based method, the user collects the information by scanning nearby WiFi(s) and transferring the information to WiFi database server. This is a fingerprint method with disadvantage of having an additional DB server. IoT is the internetworking of things, and this is on rapid rise. I propose the IoT-based indoor localization scheme. Under the proposed method, a device internetworking with another device with its own location information like GPS coordinate can estimate its own location through RSSI. With more devices localizing its own, the localization accuracy goes high. The proposed method allows the user to estimate the location without GPS and WiFi DB server.