• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.81-84
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• 2000

본 논문은 국제통신연합 전파통신분야(ITU-R)의 간섭분석기의 표준으로 채택한 몬테카를로 기법을 기초로 하여 개발된 간섭분석기를 이용하여 개인 통신 서비스(Personal Communication Service)의 도심지 환경에서 수신 전계강도를 예측하였다. 또한 실제로 측정된 수신 전계강도와 비교하여 간섭분석기의 신뢰도를 검증하였다. 개발된 간섭분석기는 한국 지형에 적합한 전파 전파 모델인 수정된 하타(Modified Hata) 모델을 적용하여 개발하였고, 국내 PCS(IS-95A) 서비스 환경과 규격을 고려한 시나리오를 설정하여 모의 실험을 수행하였다. 실험 결과 간섭 분석기와 실제 측정 수신 전계 강도 사이에는 0.03dBm의 평균오차를 가지며, 이는 간섭분석기를 통해 얻어진 결과가 실제와 매우 유사함을 보여준다.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.3
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• pp.173-182
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• 2015

A real-time, Indoor navigation systems utilize ultra-wide band (UWB), radio-frequency identification (RFID) and received signal strength (RSS) techniques that encompass WiFi, FM, mobile communications, and other similar technologies. These systems typically require surplus infrastructure for their implementation, which results in significantly increased costs and complexity. Therefore, as a solution to reduce the level of cost and complexity, an inertial measurement unit (IMU) and quick response (QR) codes are utilized in this paper to facilitate navigation with the assistance of a smartphone. The QR code helps to compensate for errors caused by the pedestrian dead reckoning (PDR) algorithm, thereby providing more accurate localization. The proposed algorithm having IMU in conjunction with QR code shows an accuracy of 0.64 m which is higher than existing indoor navigation techniques.

• Korean Management Science Review
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• v.29 no.2
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• pp.79-90
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• 2012

Focusing on the rapid market penetration of smart phones, the importance of LBS (Location Based Service) is drastically increased. However, traditional GPS method has critical weakness caused by limited availability, such as indoor environment. WPS is newly attractive method as a widely applicable positioning method. In WPS, RSSI (Received Signal Strength Indication) data of all Wi-Fi APs (Access Point) are measured and stored into a huge database. The stored RSSI data in database make single radio fingerprint map. By the radio fingerprint map, we can estimate the actual position of target point. The essential factor of radio fingerprint database is data integrity of RSSI. Because of millions of APs in urban area, RSSI measurement data are seriously contaminated. Therefore, we present the unified filtering method for RSSI measurement data. As the results of filtering, we can show the effectiveness of suggested method in practical positioning system of mobile operator.

• 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.

• Journal of Positioning, Navigation, and Timing
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• v.12 no.1
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• pp.37-42
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• 2023

Wi-Fi Received Signal Strength Indicator (RSSI) is considered one of the most important sensor data types for indoor localization. However, collecting a RSSI fingerprint, which consists of pairs of a RSSI measurement set and a corresponding location, is costly and time-consuming. In this paper, we propose a Wi-Fi RSSI learning technique without true location data to overcome the limitations of static database construction. Instead of the true reference positions, inertial measurement unit (IMU) data are used to generate pseudo locations, which enable a trainer to move during data collection. This improves the efficiency of data collection dramatically. From an experiment it is seen that the proposed algorithm successfully learns the unsupervised Wi-Fi RSSI positioning model, resulting in 2 m accuracy when the cumulative distribution function (CDF) is 0.8.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.473-480
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• 2013

In this paper, location recognition algorithm is developed to improve the accuracy using improve Trilateration. The location recognition algorithm is first calculate the location refer to the measured signal power. Error can be occurred when measure distance with arranged node in specific location. If the distance data is received from node (receiver, coordinator), Node selected for location calculation is defined through section. If the distance data is received from node (receiver, coordinator), Node selected for location calculation is defined through section. Second, we apply algorithm of section filtering. If there are 4 sections in node, we consider 1 section to 6 location recognition coordinates. A special characteristic drawback of RF is that the actual distance is actually farther than the calculated received distance data. This is error is incurred when the signal strength increases. We reduce the location recognition error by applying an improved algorithm as secondary after filtering primary through section filtering.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.5
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• pp.85-90
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• 2017

Indoor location-based services have been developed based on the Internet of Things technologies which measure and analyze users who are moving in their daily lives. These various indoor positioning technologies require separate hardware and have several disadvantages, such as a communication protocol which becomes complicated. Based on the fact that a reduction in signal strength occurs according to the distance due to the physical characteristics of the transmitted signal, RSSI technology that uses the received signal strength of the wireless signal used in this paper measures the strength of the transmitted signal and the intensity of the attenuated received signal and then calculates the distance between a transmitter and a receiver, which requires no separate costs and makes to implement simple measurements. It was applied calculating the value for the average RSSI and the RSSI filtering feedback. Filtering is used to reduce the error of the RSSI values that are measured at long distance.It was confirmed that the RSSI values through the average filtering and the RSSI values measured by setting the coefficient value of the feedback filtering to 0.5 were ranged from -61 dBm to - 52.5 dBm, which shows irregular and high values decrease slightly as much as about -2 dBm to -6 dBm as compared to general measurements.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.473-476
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• 2008

USN(Ubiquotous Sensor Network) has been applied to various fields of industries such as logistics, environment management, traffic management, as well as IT industries including home network and telematics. Among the important techniques required to implement aforementioned applications, location management scheme is essential. In this paper, we proposed and implemented a new location measurement scheme based on RSSI of sensor node for campus car location management.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4227-4240
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• 2019

In this paper, we present our proposed magnetic induction based wireless communication system. The proposed system is designed to perform communication as well as distance measurement in underground environments. In order to improve the communication quality, we propose and implement the adaptive channel compensation technique. Based on the fact that the channel may be fast time-varying, we keep track of the channel status each time the data is received and accordingly compensate the channel coefficient for any change in the channel status. By using the proposed compensation technique, the developed platform can reliably communicate over distances of 10m while the packet error rate is being maintained under 5%. We also implement the distance measurement block that is useful for various applications that should promptly estimate the location of nearby nodes in communication. The distance between two nodes in communication is estimated by generating a table describing pairs of the magnetic signal strength and the corresponding distance. The experiment result shows that the platform can estimate the distance of a node located within 10m range with the measurement error less than 50cm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.23-30
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• 2011

In the wireless sensor network(WSN), the detection of precise location of sensor nodes is essential for efficiently utilizing the sensing data acquired from sensor nodes. Among various location methods, the received signal strength (RSS) based localization scheme is mostly preferable in many applications since it can be easily implemented without any additional hardware cost. Since the RSS localization method is mainly effected by radio channel between two nodes, outlier data can be included in the received signal strength measurement specially when some obstacles move around the link between nodes. The outlier data can have bad effect on estimating the distance between two nodes such that it can cause location errors. In this paper, we propose a RSS-based localization method using Robust Statistic and Gaussian filter algorithm for enhancing the accuracy of RSS-based localization. In the proposed algorithm, the outlier data can be eliminated from samples by using the Robust Statistics as well as the Gaussian filter such that the accuracy of localization can be achieved. Through simulation, it is shown that the proposed algorithm can increase the accuracy of localization and is more robust to non gaussian noise channels.