• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.4
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• pp.1200-1215
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• 2023

With the development of IoT and artificial intelligence, location-based services are getting more and more attention. For solving the current problem that indoor positioning error is large and generalization is poor, this paper proposes a Model Stacking Algorithm for Indoor Positioning System using WiFi fingerprinting. Firstly, we adopt a model stacking method based on Bayesian optimization to predict the location of indoor targets to improve indoor localization accuracy and model generalization. Secondly, Taking the predicted position based on model stacking as the observation value of particle filter, collaborative particle filter localization based on model stacking algorithm is realized. The experimental results show that the algorithm can control the position error within 2m, which is superior to KNN, GBDT, Xgboost, LightGBM, RF. The location accuracy of the fusion particle filter algorithm is improved by 31%, and the predicted trajectory is close to the real trajectory. The algorithm can also adapt to the application scenarios with fewer wireless access points.

• Journal of Satellite, Information and Communications
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• v.9 no.3
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• pp.9-14
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• 2014

Recently, precision guided munition systems and missile defense systems based on GPS have been taking a key role in modern warfare. In warfare however, unexpected interferences cause by large/small scale fading, radio frequency interferences, etc. These interferences result in a severe GPS positioning error, which could occur late supports and friendly fires. To solve the problems, this paper proposes an interference mitigation positioning method by adopting a wavelet denoising filter algorithm. The algorithm is applied to a GPS/QZSS/Wi-Fi combined positioning system which was performed by this laboratory. Experimental results of this paper are based on a real field test data of a GPS/QZSS/Wi-Fi combined positioning system and a simulation data of a wavelet denoising filter algorithm. At the end, the simulation result demonstrates its superiority by showing a 21.6% improved result in comparison to a conventional GPS system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.10
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• pp.2195-2202
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• 2011

Recently, navigation system is used to inform users of vehicle location and driving direction, moving distance and so on. This navigation uses GPS sensor for current location determination. The GPS sensor will determinate current coordinates by using triangulation algorithm. This characteristic bring about that the GPS signal is not available in the shadow region such as tunnel and urban canyon. Moreover, Even though the signal is available, inherent positional error rate of the GPS often results in the dislocation of vehicle. To solve, these problems, a new positioning system is proposed in the paper. The System utilizes geomagnetic sensors of smartphone, speed information of CAN of vehicle though bluetooth and WiFi APs for GPS shadow area. The experimental test shadows that the proposed system using multiple data is able to determine the position of vehicle in GPS shadow areas.

• Journal of Platform Technology
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• v.11 no.5
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• pp.97-103
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• 2023

Unlike GPS, which is an outdoor positioning technology that is universally and uniformly used all over the world, various technologies are still being developed in the field of indoor positioning technology. In order to acquire accurate indoor location information, a standard of representative indoor positioning technology is required. Recently, indoor positioning technology is expanding into the Real Time Location Service (RTLS) area based on high-precision location data. Accordingly, a new type of indoor positioning technology is being proposed. Thanks to the development of artificial intelligence, artificial intelligence-based indoor positioning technology using wireless signal data of a smartphone is rapidly developing. At this time, in the process of collecting data necessary for artificial intelligence learning, data that is distorted or inappropriate for learning may be included, resulting in lower indoor positioning accuracy. In this study, we propose a data preprocessing technology for artificial intelligence learning to obtain improved indoor positioning results through the refinement process of the collected data.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.1
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• pp.129-134
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• 2018

Research on the technique for estimating the indoor position has been actively carried out. In particular, the WiFi fingerprint method, which does not require any additional infrastructure, is being partially used because of its high economic efficiency. The KNN method which estimates similar points to the corresponding points by comparing intensity information of the WLAN reception signal measured at various points in advance with intensity information measured at a specific point in the future is simple but has a good performance. However, in the conventional KNN scheme, since the number K of average candidate positions is constant, there is a problem that the position estimation error is not optimized according to a specific point. In this paper, we proposed an algorithm that adaptively changes the K value for each point and applied it to experimental data and evaluated its performance.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.149-156
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• 2014

Floor number determination has attracted considerable attention because many applications require accurate floor number information for providing better localization services in multistory buildings. This paper describes a Wi-Fi-based, low-complexity floor number determination method for multistory buildings. In our scheme, with the exception of floor ID and received signal strength of each WAP, detailed information on the wireless access point (WAP) coordinates is not needed. The multi-wall-floor model is used for the simulation and analysis. The simulation results show that the floor determination accuracy is nearly 100 given an adequate deployment density of WAPs on each floor. The results also show that the proposed method provides a good estimate of floor number even when only a few WAPs are implemented on each floor. Specifically, the proposed method can work under extreme conditions, i.e., where there are no WAPs on a floor.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.4
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• pp.1-7
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• 2020

Over the past few years, Wi-Fi signal based indoor positioning system (IPS) has been researched extensively because of its low expenses of infrastructure deployment. There are two major aspects of location-related information contained in Wi-Fi signals. One is channel state information (CSI), and one is received signal strength indicator (RSSI). Compared to the RSSI, the CSI has been widely utilized because it is able to reveal fine-grained information related to locations. However, the conventional IPS that employs a single access point (AP) does not exhibit decent performance especially in the environment of non-line-of-sight (NLOS) situations due to the reliability degeneration of signals caused by multipath fading effect. In order to address this problem, in this paper, we propose a novel method that utilizes multiple APs instead of a single AP to enhance the robustness of the IPS. In our proposed method, a hybrid neural network is applied to the CSIs collected from multiple APs. By relying more on the fingerprint constructed by the CSI collected from an AP that is less affected by the NLOS, we find that the performance of the IPS is significantly improved.

• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.3
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• pp.117-123
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• 2022

Unlike the satellite-based outdoor positioning system, the indoor positioning system utilizes various wireless technologies such as BLE, Wi-Fi, and UWB. BLE-based beacon technology can measure the user's location by periodically broadcasting predefined device ID and location information and using RSSI from the receiving device. Existing BLE-based indoor positioning system studies have many studies comparing the error between the user's actual location and the estimated location at a single point. In this paper, we propose a technique to evaluate the positioning accuracy according to the movement path or area by applying the entropy analysis model. In addition, simulation results show that calculated entropy results for different paths can be compared to assess which path is more accurate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.863-865
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• 2015

A method for estimating position is AOA, TOA, TDOA, Wi-Fi, Beacon etc. A method for estimating the location in indoor environment is used mainly Wi-Fi, Beacon. The reason is that AOA, TOA and TDOA are unfit to estimate position in indoor environment. To address this problem, this paper presents a AOA algorithm based on AP having a four directional antenna. The algorithm uses only the angle received from the four antennas. This can draw linear equations for signal. And calculate the intersections of the lines. Intersections means the position of user.