• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1197-1209
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• 2016

As Indoor Positioning System demands due to increased penetration and utilization of smart device, Indoor Positioning System using Wi-Fi or BLE(Bluetooth Low Energy) beacon takes center stage. In this paper, a terminal location of the user is calculated through Microscopic Trilateration using RSSI based on BLE. In the next step, a fingerprinting map appling approximate value of Microscopic Trilateration increases an efficiency of computation amount and energy for Indoor Positioning System. I suggest Indoor Positioning Algorithm based on BLE fingerprinting considering efficiency of energy by conducting precise Trilateration that assure user's terminal position by using AP(Access Point) surrounding targeted fingerprinting cells. And This paper shows experiment and result based on An Suggesting Algorithm in comparison with a fingerprinting based on BLE and Wi-Fi that be used for Indoor Positioning System.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.1205-1207
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• 2015

Fingerprinting 방식에서 KNN은 WLAN 기반 실내 측위에 가장 많이 적용되고 있지만 KNN의 성능은 k개의 이웃 수와 RP의 수에 따라 민감하다. 논문에서는 KNN 성능을 향상시키기 위해 ANN 군집화를 적용한 KNN과 ANN을 혼합한 알고리즘을 제안하였다. 제안한 알고리즘은 신호잡음비 데이터를 KNN 방법에 적용하여 k개의 RP을 선택한 후 선택된 RP의 신호잡음비를 ANN에 적용하여 k개의 RP를 군집하여 분류한다. 실험 결과에서는 위치 오차가 2m 이내에서 KNN/ANN 알고리즘이 KNN 알고리즘보다 성능이 우수하다.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1059-1068
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• 2021

In urban areas, GPS(Global Positioning System)/GNSS(Global Navigation Satellite System) signals are blocked or distorted by structures such as buildings, which limits positioning. To compensate for this problem, in this paper, fingerprinting-based positioning using RSRP(: Reference Signal Received Power) information of LTE signals is performed. The W-KNN(Weighted - K Nearest Neighbors) technique, which is widely used in the positioning step of fingerprinting, yields different positioning performance results depending on the similarity distance calculation method and weighting method used in correlation. In this paper, the performance of the fingerprinting positioning according to the techniques used in correlation is comparatively analyzed experimentally.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.11
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• pp.1549-1556
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• 2016

By increased moving speed of aircraft, estimating location of itself becomes more important than ever. This requirement is satisfied by appearance of GPS, however it is useless when signal reception from satellite is not good enough by interruption, for example, traffic jamming. Applying link for communication to additional positioning system is capable of providing relative position of aircraft. Estimating location with link for communication is done without additional equipment but with signal processing based on correlation of received signal. ESPRIT is one of the representative algorithm among them. Estimating correlation matrix is possible to have error since it includes average operation needs enough number of samples not impractical. Therefore we propose algorithm that defines, estimates and removes error matrix of correlation. Proposing algorithm shows better performance than previous one when transmitters are close.

• Journal of Advanced Navigation Technology
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• v.19 no.6
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• pp.558-563
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• 2015

There have recently been various services that use indoor location estimation technologies. Representative methods of location estimation include fingerprinting and triangulation, but they lack accuracy. Various kinds of research which apply existing location estimation methods like AOA, TOA, and TDOA are being done to solve this problem. In this paper, we study the location estimation algorithm based on AOA using a RSSI difference in indoor environments. We assume that there is a single AP with four antennas, and estimate the angle of arrival based on the RSSI value to apply the AOA algorithm. To compensate for RSSI, we use a recursive averaging filter, and use the corrected RSSI and the Pythagorean theorem to estimate the angle of arrival. The results of the experiment, show an error of 18% because of the radiation pattern of the four non-directional antennas arranged at narrow intervals.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.3
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• pp.448-458
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• 2020

Indoor positioning based on trilateration using common chord estimates location of a mobile subject by using intersection points between each circles which the radius is same as distance between the mobile subject and each radio-frequency transmitter. However, if the intersection points are not found due to error of the distance measurement, it causes failure of estimating the mobile subject's location. To prevent this case, numbers which is proportionate to radius of each circles, are temporarily added to each distances in order to lengthen radius of the circles. Although the estimated location includes error due to the radius extension, it is corrected again by the added value and distance from reference point. With introduction of the advanced correction algorithm, potential issues of existing trilateration such as failure of estimating location and distance measurement error will be minimized.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.4
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• pp.601-608
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• 2023

Mobile robots are widely used in industries because mobile robots perform tasks in various environments. In order to carry out tasks, determining the precise location of the robot in real-time is important due to the need for path generation and obstacle detection. In particular, when mobile robots autonomously navigate in indoor environments and carry out assigned tasks within pre-determined areas, highly precise positioning performance is required. However, mobile robots frequently experience data missing in wireless communication environments. The robots need to rely on predictive techniques to autonomously determine the mobile robot positions and continue performing mobile robot tasks. In this paper, we propose an extended Kalman filter-based algorithm to enhance the accuracy of mobile robot localization and address the issue of data missing. Trilateration algorithm relies on measurements taken at that moment, resulting in inaccurate localization performance. In contrast, the proposed algorithm uses residual values of predicted measurements in data missing environments, making precise mobile robot position estimation. We conducted simulations in terms of data missing to verify the superior performance of the proposed algorithm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.27 no.4
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• pp.461-467
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• 2009

Relative positioning technique by GPS that can obtain the high positioning accuracy has been used for generation of high precision positioning with elimination or the reduction of the common errors. This paper gives some algorithms for RTK and considers the filter to estimate the positioning information and integer ambiguities at each epoch in the whole algorithms. The extended kalman filter has been employed to estimate the state parameters and the modified LAMBDA to resolve the integer ambiguities. The data processing was performed by GPS single frequency and dual frequency in short baseline. The verification procedure of these positioning compared with results from Bernese 5.0 software. We presented some statistic values on positioning errors and the rates of integer ambiguity resolution.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.88-91
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• 2016

최근 스마트 기기의 활용 요구 증가로 인한 실내 위치 인식 시스템 수요가 급증함에 따라, BLE(Bluetooth Low Energy) 비콘을 이용한 실내 측위 시스템이 각광받고 있다. 본 논문은 BLE 비콘 기반에 중심을 두고 RSSI 신호를 이용하여 거시적인 삼변 측량 기법을 이용하여 산출한다. 그 결과값을 근사치 위치에만 Fingerprinting을 적용하여 위치 측위 기본 연산량을 줄임과 동시에 에너지 효율을 증대시킨다. 또한 선정된 Fingerprinting Cell 주위의 노드만을 이용하여 사용자의 단말 위치의 정밀성을 보장하는 정밀 삼변 측량 연산을 수행하여 에너지 효율을 고려한 BLE 핑거프린팅 기반의 정밀 실내 측위 알고리즘을 제안한다.