위치기반서비스를 위한 옥내 이동객체 데이터베이스 갱신전략: 칼만 필터 방법 (Updating Policy of Indoor Moving Object Databases for Location-Based Services: The Kalman Filter Method)

서지정보

· 발행기관 : 한국정보시스템학회
· 수록지 정보 : 정보시스템연구 / 19권 / 1호 / 1 ~ 17페이지
· 저자명 : 임재걸, 주재훈, 박찬식, 권기용, 김민혜

초록

This paper proposes an updating policy of indoor moving object databases (IMODB) for location-based services. Our method applies the Kalman filter on the recently collected measured positions to estimate the moving object's position and velocity at the moment of the most recent measurement, and extrapolate the current position with the estimated position and velocity. If the distance between the extrapolated current position and the measured current position is within the threshold, in other words if they are close then we skip updating the IMODB. When the IMODB needs to know the moving object's position at a certain moment T, it applies the Kalman filter on the series of the measurements received before T and extrapolates the position at T with the estimations obtained by the Kalman filter. In order to verify the efficiency of our updating method, we performed the experiments of applying our method on the series of measured positions obtained by applying the fingerprinting indoor positioning method while we are actually walking through the test bed. In the analysis of the test results, we estimated the communication saving rate of our method and the error increment rate caused by the communication saving.

영어초록

This paper proposes an updating policy of indoor moving object databases (IMODB) for location-based services. Our method applies the Kalman filter on the recently collected measured positions to estimate the moving object's position and velocity at the moment of the most recent measurement, and extrapolate the current position with the estimated position and velocity. If the distance between the extrapolated current position and the measured current position is within the threshold, in other words if they are close then we skip updating the IMODB. When the IMODB needs to know the moving object's position at a certain moment T, it applies the Kalman filter on the series of the measurements received before T and extrapolates the position at T with the estimations obtained by the Kalman filter. In order to verify the efficiency of our updating method, we performed the experiments of applying our method on the series of measured positions obtained by applying the fingerprinting indoor positioning method while we are actually walking through the test bed. In the analysis of the test results, we estimated the communication saving rate of our method and the error increment rate caused by the communication saving.

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