• 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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.2
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• pp.181-188
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• 2013

In this paper, we present a system design for location determination inside the ship. Since the GPS signal can not be received in the interior of the large vessel, we use the vessel wireless AP (Access Point) RSSI (received signal strength indication) to accurately measure the position. We convert the RSSI for the 3 AP's into distance through the Friis formula and get the location through triangulation. The signal strength varies irregularly due to noise making it difficult to obtain the exact location. Thus Kalman filter is used to real-time position correction, that is store in a server database.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.2
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• pp.158-163
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• 2010

In this paper, we propose the location measurement algorithm of unknown tag based on RFID (Radio-Frequency IDentification) by using RSSI (Received Signal Strength Indication) and TDOA (Time Difference of Arrival) and extended Kalman filter in smart space. To do this, first, we recognize the location of unknown tag by using the RSSI and TDOA recognition methods. Second, we set the coordinate of the tag location measured by using trilateration and SX algorithm. But the tag location data measured by this method are included complex environmental error. So, we use the extended Kalman filter in order to revise error data of the tag location. Finally, we validate the applicability of the proposed method though the simulation in a complex environment.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.20 no.9
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• pp.954-960
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• 2009

In this paper, Indoor Positioning System based on Wi-Fi system which is one of the key technology in LBS(Location Based Service) is proposed. The proposed system estimates distance between MS(Mobile Station) and AP(Access Point) using RSSI(Received Signal Strength Indicator). RSSI is affected by outlier that originate from indoor environment complexity and obstacle. In this paper, we introduce a Robust outlier Extended Kalman Filter that can ignore, real-time outlier in the observations. To demonstrate performance of proposed indoor positioning system, we used a PDA as the MS.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.147-153
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• 2018

Beacon enables one to measure distance indoors based on low-power Bluetooth low energy (BLE) technology, while GPS (Global Positioning System) only can be used outdoors. In measuring indoor distance using Beacon, RSSI (Received Signal Strength Indication) is considered as the one of the key factors, however, it is influenced by various environmental factors so that it causes the huge gap between the estimated distance and the real. In order to handle this issue, we propose the adaptive ANN (Artificial Neural Network) based approach to measuring the exact distance using Beacon. First, we has carried out the preprocessing of the RSSI signals by applying the extended Kalman filter and the signal stabilization filter into decreasing the noise. Then, we suggest the multi-layered ANNs, each of which layer is learned by specific training data sets. The results showed an average error of 0.67m, a precision of 0.78.

• Proceedings of the Korean Society of Computer Information Conference
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• 2017.01a
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• pp.27-28
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• 2017

본 논문은 RSSI 값을 이용한 Zigbee Commissioning 기법에 대하여 서술한다. Zigbee는 대표적인 무선 통신 표준 기술로 다양한 분야에서 활용 하고 있으며, Zigbee를 이용한 Commissioning은 주로 Smart Led와 같은 분야에서 활용되고 있다. Zigbee Commissioning의 주된 과제는 기기와 네트워크를 구성하는 것에 있는데 RSSI신호에 민감하여 각 디바이스를 식별할 때에 문제점이 있다. 본 논문에서는 RSSI값을 제안하는 알고리즘을 이용하여 효율적으로 Zigbee Commissioning하는 기법을 서술한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.180-182
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• 2021

This paper proposes a method for estimating the location of a user using a beacon to provide a service in an indoor environment. To estimate the location using the beacon, a Gaussian filter was applied to the RSSI value of the beacon, and the distance conversion function was obtained through the filtered RSSI value to estimate the tag location by trilateration. Then, in the indoor space where the beacons are installed, the location estimation accuracy of 8 places where 3 beacons are at a certain distance was confirmed. As a result, it was possible to confirm the position estimation accuracy of ±0.097 standard deviation and 0.242 distance error.

• 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 Society of Computer and Information
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• v.24 no.5
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• pp.67-72
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• 2019

The self-driving cars identify appropriate navigation paths and obstacles to arrive at their destinations without human control. The autonomous cars are capable of sensing driving environments to improve driver and pedestrian safety by sharing with neighbor traffic infrastructure. In this paper, we have focused on pedestrian protection and have designed an improved localization algorithm to track mobile users on roads by interacting with smart traffic lights in vehicle environments. We developed smart traffic lights with the RSSI sensor and built the proposed method by improving the Kalman filter algorithm to localize mobile users accurately. We successfully evaluated the proposed algorithm to improve the mobile user localization with deployed five smart traffic lights.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1551-1558
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• 2021

Beacon signals used in indoor positioning system are reflected and distorted, resulting in noise signals. KF(Kalman Filter) has been widely used to remove this noise. In order to apply the KF, optimization process considering the signal type, signal strength, and environmental elements of each product is required. In this paper, we propose a solution to the optimization problem of KF Parameters using GA(Genetic Algorithm) in BLE(Bluetooth Low Energy) Beacon-based indoor positioning system. After optimizing KF Parameters by applying the proposed technique with a certain distance between Beacon and receiver, we compared the estimated distance passed through KF with the unfiltered distance. The proposed technique is expected to reduce the time required and improve accuracy of KF Parameters optimization in an indoor positioning system based on RSSI (Received Signal Strength Indication).