Usability of open-source hardware based platform for indoor positioning systems

The application of indoor location systems (also known as indoor positioning systems, IPS) has significantly increased in the past decade. Those systems find their role in the broad range of possible implementations, especially in the applications in the area of resource management and location tracking. Their applications can be in safety management, material, construction, and inventory management. Since, those systems are designed for indoor and closed areas where GPS, GLONASS, and other navigation systems are not applicable, a different approach should be used to determine the location. This paper presents a brief overview of indoor localization technologies and methods. The focus of this paper is on RSSI based methods for positioning. The contribution of this paper is in analyses of usability of platforms built on Arduino/Genuino development boards and similar devices and open-source hardware for usage in RSSI based indoor positioning systems. The presented platform is designed and evaluated with the two experiments, with two different technologies

A Preliminary Cut-off Indoor Positioning Scheme Using Relative Ranks between Peaks of RSSI

Recently, the indoor positioning schemes have been actively studied. The indoor positioning scheme can be roughly classified into four schemes using triangulation, fingerprint map, proximity or visualization, respectively. This paper introduces the preliminary cut-off indoor positioning scheme using the fingerprint map. The preliminary cut-off scheme improves the accuracy of the K-Nearest-Neighbor (KNN) algorithm, which is a typical scheme of using the fingerprint map. This scheme consists of two phases: off-line and on-line phases. The off-line phase constitutes a fingerprint map necessary for real-time positioning. APs that periodically generate a signal are arranged and reference points predefined in indoor environment are selected. Then, the RSSIs received from nearby beacons at each reference point are stored in the fingerprint map. The on-line phase actually estimates indoor position. The user’s device receives the signal of the nearby APs. User’s position is estimated by comparing the RSSI received in real-time and RSSI stored in the fingerprint map. The KNN algorithm uses the Euclidean distance to compare the RSSI received in real-time and the RSSI stored in the fingerprint map. The K reference points with the shortest distance are selected and the position of user is estimated as the center of these reference points. However, since there are many obstacles in the indoor environment, the strength of signal is not constant even at the same position. To mitigate the instability and variability of the radio signal, the preliminary cut-off scheme utilizes the relative rank of the peak of signal strength, not the signal strength. Then, the user’s position is estimated as the center of K reference points with the greatest similarity after calculating the similarity between the real-time ranking and the ranking of the fingerprint map. This paper describes a continually improved study to improve the accuracy of the preliminary cut-off scheme. As a result, not only similarity to the relative rank of the peak of the signal strength but also the similarity to the peak is calculated and a weight based on this similarity is assigned. The user’s position is estimated to be the calculated position by weighting each reference position, not the center of the K reference positions.List of Tables iv List of Figures vi Abstract viii 제 1 장 서 론 1 제 2 장 관련 연구 4 2.1 삼각측량법을 이용한 실내 위치 추정 방식 4 2.2 핑거프린트 지도를 이용한 실내 위치 추정 방식 5 2.3 보행자의 걸음과 스마트폰을 이용한 실내 위치 추정 방식 8 제 3 장 비콘과 참조위치의 배치 관계 10 3.1 비콘과 참조위치의 배치에 대한 중요성 10 3.2 비콘과 참조위치 배치 간격에 대한 실험 10 3.2.1 실험 환경 10 3.2.2 실험 결과 12 3.3 최적의 비콘과 참조위치 배치 간격 14 제 4 장 핑거프린트 지도 구성방식 15 4.1 기존 핑거프린트 지도 구성 방식의 문제점 15 4.2 컷-오프 핑거프린트 지도 구성 방식 16 4.2.1 개요 16 4.2.2 실험 환경 및 구성 18 4.2.3 실험 결과 19 4.3 여러 컷-오프 지도를 조합하는 핑거프린트 지도 구성방식 26 4.3.1 개요 26 4.3.2 실험 및 성능 평가 28 제 5 장 핑거프린트 지도를 이용한 사전 컷-오프 방식 32 5.1 사전 컷-오프 실내 위치 추정 방식 32 5.2 이웃 참조 위치가 없는 경우를 개선한 사전 컷-오프 방식 36 5.3 신호유사도가 일치하는 참조 위치를 고려한 사전 컷-오프 방식 40 5.4 최대 RSSI 간의 유사도를 기반으로 한 가중치를 부여한 사전 컷-오프 방식 43 제 6 장 실험 및 성능 평가 46 6.1 사전 컷-오프 실내 위치 추정 방식 46 6.1.1 실험 환경 및 구성 46 6.1.2 실험 결과 47 6.2 이웃 참조 위치가 없는 경우를 개선한 사전 컷-오프 방식 51 6.2.1 실험 환경 및 구성 51 6.2.2 실험 결과 52 6.3 신호유사도가 일치하는 참조 위치를 고려한 사전 컷-오프 방식 57 6.3.1 실험 환경 및 구성 57 6.3.2 실험 결과 58 6.4 최대 RSSI간의 유사도를 기반으로 한 가중치를 부여한 사전 컷-오프 방식 62 6.4.1 실험 환경 및 구성 62 6.4.2 실험 결과 62 제 7 장 결 론 66 참고문헌 68Maste

Wireless Localization Based on RSSI Fingerprint Feature Vector

RSSI wireless signal is a reference information that is widely used in indoor positioning. However, due to the wireless multipath influence, the value of the received RSSI will have large fluctuations and cause large distance error when RSSI is fitted to distance. But experimental data showed that, being affected by the combined factors of the environment, the received RSSI feature vector which is formed by lots of RSSI values from different APs is a certain stability. Therefore, the paper proposed RSSI-based fingerprint feature vector algorithm which divides location area into grids, and mobile devices are localized through the similarity matching between the real-time RSSI feature vector and RSSI fingerprint database feature vectors. Test shows that the algorithm can achieve positioning accuracy up to 2–4 meters in a typical indoor environment