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Open AccessArticle

Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors

Chengdu Engineering Corporation Limited, Chengdu 610072, China
Department of Engineering Geology and Hydrogeology, RWTH Aachen University, Aachen 52064, Germany
Department of Applied Geosciences, TU Berlin University, Berlin 10587, Germany
Author to whom correspondence should be addressed.
Academic Editor: Jörg F. Wagner
Sensors 2016, 16(7), 1109;
Received: 25 March 2016 / Revised: 25 May 2016 / Accepted: 1 June 2016 / Published: 19 July 2016
(This article belongs to the Collection Modeling, Testing and Reliability Issues in MEMS Engineering)
The fast development of wireless sensor networks and MEMS make it possible to set up today real-time wireless geotechnical monitoring. To handle interferences and noises from the output data, Kalman filter can be selected as a method to achieve a more realistic estimate of the observations. In this paper, a one-day wireless measurement using accelerometers and inclinometers was deployed on top of a tunnel section under construction in order to monitor ground subsidence. The normal vectors of the sensors were firstly obtained with the help of rotation matrices, and then be projected to the plane of longitudinal section, by which the dip angles over time would be obtained via a trigonometric function. Finally, a centralized Kalman filter was applied to estimate the tilt angles of the sensor nodes based on the data from the embedded accelerometer and the inclinometer. Comparing the results from two sensor nodes deployed away and on the track respectively, the passing of the tunnel boring machine can be identified from unusual performances. Using this method, the ground settlement due to excavation can be measured and a real-time monitoring of ground subsidence can be realized. View Full-Text
Keywords: Kalman filter; ground subsidence; rotation matrices; accelerometer; inclinometer Kalman filter; ground subsidence; rotation matrices; accelerometer; inclinometer
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MDPI and ACS Style

Li, C.; Azzam, R.; Fernández-Steeger, T.M. Kalman Filters in Geotechnical Monitoring of Ground Subsidence Using Data from MEMS Sensors. Sensors 2016, 16, 1109.

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