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New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System.
Lee, Jae Kang; Lee, Jae One; Kim, Jung Ok.
Afiliação
  • Lee JK; LX Spatial Information Research Institute, 163, Anjeon-ro, Iseo-myeon, Wanju_Gun, Jeollabuk-do 55365, Korea. jaekang.lee@lx.or.kr.
  • Lee JO; Department of Civil Engineering, Dong-A University, Busan 49315, Korea. leejo@dau.ac.kr.
  • Kim JO; Institute of Construction and Environmental Engineering, Seoul National University, Seoul 08826, Korea. geostar1@snu.ac.kr.
Sensors (Basel) ; 16(6)2016 May 27.
Article em En | MEDLINE | ID: mdl-27240375
ABSTRACT
This research introduces an improvement plan for the reliability of Global Navigation Satellite System (GNSS) positioning solutions. It should be considered the most suitable methodology in terms of the adjustment and positioning of GNSS in order to maximize the utilization of GNSS applications. Though various studies have been conducted with regards to Bridge Health Monitoring System (BHMS) based on GNSS, the outliers which depend on the signal reception environment could not be considered until now. Since these outliers may be connected to GNSS data collected from major bridge members, which can reduce the reliability of a whole monitoring system through the delivery of false information, they should be detected and eliminated in the previous adjustment stage. In this investigation, the Detection, Identification, Adaptation (DIA) technique was applied and implemented through an algorithm. Moreover, it can be directly applied to GNSS data collected from long span cable stayed bridges and most of outliers were efficiently detected and eliminated simultaneously. By these effects, the reliability of GNSS should be enormously improved. Improvement on GNSS positioning accuracy is directly linked to the safety of bridges itself, and at the same time, the reliability of monitoring systems in terms of the system operation can also be increased.
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Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2016 Tipo de documento: Article

Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2016 Tipo de documento: Article