• 한국정보과학회논문지:소프트웨어및응용
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• 제33권12호
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• pp.1022-1030
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• 2006

위치 인식은 유비쿼터스 컴퓨팅 환경상의 중요한 부분이지만 많은 연구에도 불구하고 아직 완벽한 시스템은 존재하지 않는 상황이다. 본 연구에서는 다양한 위치 추적 시스템 중 가장 널리 사용되는 RFID 시스템을 이용하지만 수신된 RSSI 신호는 리더와 태그 안테나의 방향, 각도, 간섭에 매우 민감하여 기존 알고리즘인 파티클 필터를 이용하면 정확한 위치 추정이 힘들다. 이를 극복하기 위해, 본 연구에서는 이중 구조의 파티클 필터를 가진 강인한 위치 추적 시스템을 제안한다. 이 시스템은 하단부에서 회귀분석이나 SVM 분류기법을 이용하여 대략적인 위치를 확인한 다음, 상단부에서 파티클 필터를 이용하여 위치, 속도, 방향을 추정하는 계층적 구조를 갖고 있다. 그리고 계층 구조상에 움직임 특성이 갖는 여러 제약 사항을 반영하여 위치 추정 성능을 향상시킨다. 제안한 위치 추정 시스템을 실제 상황에 적용하고자 리더와 서버간을 스타 메쉬 네트워크로 연결하여 태그를 소지한 사람과 물체의 위치를 제안한 알고리즘을 이용하여 추정하였다. 실험 결과 제안한 위치 추적 시스템이 기존의 파티클 필터를 이용한 시스템보다 정확한 위치 추정 성능을 보임을 확인하였고 지하 시설물이 복잡하게 놓여있는 매우 열악한 운영 환경상에서도 실시간 동작을 통해 그 유용성이 입증되었다.

• 한국컴퓨터정보학회논문지
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• 제21권10호
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• pp.55-62
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• 2016

In this paper, we present indoor localization technologies using the bluetooth signal categorizing them into proximity based, triangulation based and fingerprinting based technologies. Then we provide localization accuracy improvement algorithms such as moving average, K-means, particle filter, and K-Nearest neighbor algorithms. We define important performance issues for indoor localization technologies and analyze recent technologies according to the performance issues. Finally we provide experimental results for correlation between RSSI and distance. We believe that this paper provide wise view and necessary information for recent localization technologies using the bluetooth signal.

• 제어로봇시스템학회논문지
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• 제18권8호
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• pp.725-730
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• 2012

In this paper, we propose the localization algorithm for the cooperative behavior of the swarm robots based on WSN (Wireless Sensor Network). The proposed method is as follows: First, we measure positions of the L-bot (Leader robot) and F-bots (Follower robots) by using the APIT (Approximate Point In Triangle) and the RSSI (Received Signal Strength Indication). Second, we measure relative positions of the F-bots against the pre-measured position of the L-bot by using trilateration. Then, to revise a position error caused by noise of the wireless signal, we use the particle filter. Finally, we show the effectiveness and feasibility of the proposed method though some simulations.

• 대한임베디드공학회논문지
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• 제16권5호
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• pp.195-200
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• 2021

In this paper, we propose a method for a mobile robot to estimate a specific location of a service provision target using a beacon-tag for the purpose of providing location-based services (LBS) to users in an indoor environment. To estimate the location, the irregular characteristics and error factors of the received signal strength indicator (RSSI) generated from the beacon are analyzed, and the distance conversion function is derived from the RSSI data extracted by applying a Gaussian filter. Then, the distance data converted from the plurality of beacons estimates an indoor location through a triangulation technique. After that, the improvement in the location estimation is analyzed by applying the temporal confidence reasoning technique. The possibility of providing a LBS of a mobile robot was confirmed through a location estimation experiment for a plurality of designated locations in an indoor environment.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2007년도 춘계학술발표대회
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• pp.1473-1476
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• 2007

유비쿼터스 컴퓨팅과 유비쿼터스 네트워크가 다양하게 발전되고 활용됨에 따라 유비쿼터스 환경에서 LBS(위치 기반서비스) 관련 기술의 중용성도 높아지고 있다. 이런 LBS를 위한 기존의 대표적인 옥내 측위 시스템들을 알아보고, IEEE802.11b 규격의 2.4GHz RF의 RSSI와 거리와의 관계를 기반으로 TDOA를 이용한 방법과 Extended Kalman Filter를 이용한 방법의 측위 시스템을 구현하고 실험을 통한 실측 오차를 비교 분석한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권5호
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• pp.1339-1355
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• 2023

Localization is a hot research spot for many areas, especially in the mobile robot field. Due to the weak signal of the global positioning system (GPS), the alternative schemes in an indoor environment include wireless signal transmitting and receiving solutions, laser rangefinder to build a map followed by a re-localization stage and visual positioning methods, etc. Among all wireless signal positioning techniques, Wi-Fi is the most common one. Wi-Fi access points are installed in most indoor areas of human activities, and smart devices equipped with Wi-Fi modules can be seen everywhere. However, the localization of a mobile robot using a Wi-Fi scheme usually lacks orientation information. Besides, the distance error is large because of indoor signal interference. Another research direction that mainly refers to laser sensors is to actively detect the environment and achieve positioning. An occupancy grid map is built by using the simultaneous localization and mapping (SLAM) method when the mobile robot enters the indoor environment for the first time. When the robot enters the environment again, it can localize itself according to the known map. Nevertheless, this scheme only works effectively based on the prerequisite that those areas have salient geometrical features. If the areas have similar scanning structures, such as a long corridor or similar rooms, the traditional methods always fail. To address the weakness of the above two methods, this work proposes a coarse-to-fine paradigm and an improved localization algorithm that utilizes Wi-Fi to assist the robot localization in a geometrically similar environment. Firstly, a grid map is built by using laser SLAM. Secondly, a fingerprint database is built in the offline phase. Then, the RSSI values are achieved in the localization stage to get a coarse localization. Finally, an improved particle filter method based on the Wi-Fi signal values is proposed to realize a fine localization. Experimental results show that our approach is effective and robust for both global localization and the kidnapped robot problem. The localization success rate reaches 97.33%, while the traditional method always fails.

• Journal of Advanced Marine Engineering and Technology
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• 제39권2호
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• pp.179-186
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• 2015

In the indoor location estimation system, which has recently been actively studied, the received signal strength indicator contains a high level of noise when measuring the signal strength in the range between two nodes consisting of a receiver and a transceiver. To minimize the noise level, this paper proposes an improved adaptive smoothing filter that provides different exponential weights to the current value and previous averaged one of the data that were obtained from the nodes, because the characteristic signal attenuation of the received signal strength indicator generally has a log distribution. The proposed method can effectively decrease the noise level by using a feedback filter that can provide different weights according to the noise level of the obtained data and thus increase the accuracy in the distance and location without an additional filter such as the link quality indicator, which can verify the communication quality state to decrease the range errors in the indoor location recognition using ZigBee based on IEEE 802.15.4. For verifying the performance of the proposed improved adaptive smoothing filter, actual experiments are conducted in three indoor locations of different spatial sections. From the experimental results, it is verified that the proposed technique is superior to other techniques in range measurement.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.92-102
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• 2017

In this paper, we consider a typical health care system via the help of Wireless Sensor Network (WSN) for wireless patient tracking. The wireless patient tracking module of this system performs localization out of samples of Received Signal Strength (RSS) variations and tracking through a Particle Filter (PF) for WSN assisted by multiple transmit-power information. We propose a modified PF, Kullback-Leibler Distance (KLD)-resampling PF, to ameliorate the effect of RSS variations by generating a sample set near the high-likelihood region for improving the wireless patient tracking. The key idea of this method is to approximate a discrete distribution with an upper bound error on the KLD for reducing both location error and the number of particles used. To determine this bound error, an optimal algorithm is proposed based on the maximum gap error between the proposal and Sampling Important Resampling (SIR) algorithms. By setting up these values, a number of simulations using the health care system's data sets which contains the real RSSI measurements to evaluate the location error in term of various power levels and density nodes for all methods. Finally, we point out the effect of different power levels vs. different density nodes for the wireless patient tracking.

• 한국정보통신학회논문지
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• 제15권5호
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• pp.1087-1096
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• 2011

지능형 차량 네트워크(VANET)는 무선통신을 이용하여 차량 간 (V2V, Vehicle to Vehicle), 차량과 노변장치 간(V2I, Vehicle to Infrastructure)의 통신을 제공하는 네트워킹 기술이다. 현재 VANET통신은 자동차산업의 급속한 발전과 차량자동화로 인하여 산업계와 학계를 중심으로 연구가 활발히 진행되고 있다. VANET을 통해 유통되는 차량의 속도, 가속도, 도로 및 환경 모니터링정보는 운전자에게 안전운전과 관련된 서비스를 제공하는 분야로써 통신에서의 보안은 필수적인 요건이다. 지금까지 안전한 메시지 인증을 위한 많은 인증프로토콜들이 제시되어 왔다. 그 중에서도 Jung에 의해 제안된 VANET 알고리즘은 데이터베이스 검색 알고리즘인 블룸 필터를 RAISE 알고리즘에 적용하여 차량 밀집환경에서의 인증에 보다 효율적인 알고리즘을 제안하였다. 하지만 RAISE에서 사용한 k-anonymity는 정확한 차량의 ID정보를 얻기 위해 모든 메시지에 대해 전수조사 연산을 수행해야 하므로 차량의 수가 증가함에 따라 해시연산량이 지수적으로 증가한다. 또한 핸드오버가 발생하는 경우 완벽한 키전달 알고리즘을 제공하지 못한다. 본 논문에서는 RSSI기반 속도 및 거리 추정 알고리즘을 사용하여 사용자의 ID를 위치화하며 프로토콜의 핸드오버부분의 오류를 수정하여 안전하고 효율적인 알고리즘을 제공한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제3권1호
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• pp.119-133
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• 2009

The purpose of this research is to accurately estimate the location of a device using the received signal strength indicator (RSSI) of IEEE 802.11 WLAN for location tracking in indoor environments. For the location estimation method, we adopted the calibration model. By applying the Adaptive Zone Based K-NNSS (AZ-NNSS) algorithm, which considers the velocity of devices, this paper presents a 9% improvement of accuracy compared to the existing K-NNSS-based research, with 37% of the K-NNSS computation load. The accuracy is further enhanced by using a Kalman filter; the improvement was about 24%. This research also shows the level of accuracy that can be achieved by replacing a subset of the calibration data with values computed by a numerical equation, and suggests a reasonable number of calibration points. In addition, we use both the mean error distance (MED) and hit ratio to evaluate the accuracy of location estimation, while avoiding a biased comparison.