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Constrained MLAMBDA Method for Multi-GNSS Structural Health Monitoring.

Authors :
Li, Haiyang
Nie, Guigen
Chen, Dezhong
Wu, Shuguang
Wang, Kezhi
Source :
Sensors (14248220). Oct2019, Vol. 19 Issue 20, p4462. 1p.
Publication Year :
2019

Abstract

Deformation monitoring of engineering structures using the advanced Global Navigation Satellite System (GNSS) has attracted research interest due to its high-precision, constant availability and global coverage. However, GNSS application requires precise coordinates of points of interest through quick and reliable resolution of integer ambiguities in carrier phase measurements. Conventional integer ambiguity resolution algorithms have been extensively researched indeed in the past few decades, although the application of GNSS to structural health monitoring is still limited. In particular, known a priori information related to the structure of a body of interest is not normally considered. This study proposes a composite strategy that incorporates modified least-squares ambiguity decorrelation adjustment (MLAMBDA) method with priori information of the structural deformation. Data from the observation sites of Baishazhou Bridge are used to test method performance. Compared to MLAMBDA methods that do not consider priori information, the ambiguity success rate (ASR) improves by 20% for global navigation satellite system (GLONASS) and 10% for Multi-GNSS, while running time is reduced by 60 s for a single system and 180 s for Multi-GNSS system. Experimental results of Teaching Experiment Building indicate that our constrained MLAMBDA method improves positioning accuracy and meets the requirements of structural health monitoring, suggesting that the proposed strategy presents an improved integer ambiguity resolution algorithm. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14248220
Volume :
19
Issue :
20
Database :
Academic Search Index
Journal :
Sensors (14248220)
Publication Type :
Academic Journal
Accession number :
139255907
Full Text :
https://doi.org/10.3390/s19204462