Structural displacement estimation through multi-rate fusion of accelerometer and RTK-GPS displacement and velocity measurements

Kim, Ki‐Young; Choi, Jaemook; Chung, Junyeon; Koo, Gunhee; Bae, Inhwan; Sohn, Hoon

doi:10.1016/j.measurement.2018.07.090

Cited by 59 publications

(21 citation statements)

References 42 publications

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“…31 GNSS, Global Navigation Satellite System; RTK, Real-Time Kinematic of about 2 mm through the multi-rate integration of accelerometer and GPS measurements. 69 Meng et al 70 proposed data fusion algorithm for multi-rate GNSS and accelerometer data using an extended Kalman filter, successfully verified by simulation data of a civil structural vibration.…”

Section: Integrated Gnss and Accelerometer Systemmentioning

confidence: 95%

“…Kim et al also presented a novel multi‐rate Kalman filtering for the dynamic estimation of integrated continuous acceleration and intermittent displacement measurements. The structural displacements were successfully estimated with an accuracy of about 2 mm through the multi‐rate integration of accelerometer and GPS measurements . Meng et al proposed data fusion algorithm for multi‐rate GNSS and accelerometer data using an extended Kalman filter, successfully verified by simulation data of a civil structural vibration.…”

Section: Innovative Techniques Of Gnss Data Acquisitionmentioning

confidence: 99%

See 1 more Smart Citation

Global Navigation Satellite System‐based positioning technology for structural health monitoring: a review

Meng

Yan

et al. 2019

Struct Control Health Monit

115

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Summary Global Navigation Satellite System (GNSS) positioning technology has had widespread applications in the structural health monitoring as its overall performance has improved significantly in the last two decades. It is capable of providing timely and accurate structural vibration information such as dynamic displacements and modal frequencies at higher performance than traditional accelerometers. The studies summarized in this paper focus on the improvement of the multi‐sensors and multi‐constellation data acquisition techniques, the improvement of multiple approaches for erroneous noise mitigation, and innovative modal parameter identification methods. We also detailed the applications of GNSS on the deformation monitoring for towers, chimneys, tall buildings, and bridges. With continuous enhancements in the algorithm and hardware of GNSS, it is expected that the application of GNSS technology can be expanded to other fields such as bridge cable‐force measurements and bridge weight‐in‐motion as well as structural deformation monitoring.

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Section: Integrated Gnss and Accelerometer Systemmentioning

confidence: 95%

Section: Innovative Techniques Of Gnss Data Acquisitionmentioning

confidence: 99%

Global Navigation Satellite System‐based positioning technology for structural health monitoring: a review

Meng

Yan

et al. 2019

Struct Control Health Monit

115

View full text Add to dashboard Cite

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“…An overview of the test configuration is shown in Figure 4 . In this experiment, a set of sensor systems developed by authors and Poongsan FNS Co. [ 29 ] was adopted. The sensor system consists of a sensor module, a combination of a triaxial accelerometer, an RTK-GNSS rover chipset and an antenna, and a base module which has an RTK-GNSS base chipset and an antenna.…”

Section: Lab-scale Experimentsmentioning

confidence: 99%

“…The angle of the panel was adjusted so that the incident angle was close to 90°. The measured out-of-plane movement of the panel can be converted to the vertical displacement of the measurement point using simple trigonometry [ 29 ].…”

Section: Field Testsmentioning

confidence: 99%

Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Kim

Sohn

2020

Sensors

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In this paper, we propose a dynamic displacement estimation method for large-scale civil infrastructures based on a two-stage Kalman filter and modified heuristic drift reduction method. When measuring displacement at large-scale infrastructures, a non-contact displacement sensor is placed on a limited number of spots such as foundations of the structures, and the sensor must have a very long measurement distance (typically longer than 100 m). RTK-GNSS, therefore, has been widely used in displacement measurement on civil infrastructures. However, RTK-GNSS has a low sampling frequency of 10–20 Hz and often suffers from its low stability due to the number of satellites and the surrounding environment. The proposed method combines data from an RTK-GNSS receiver and an accelerometer to estimate the dynamic displacement of the structure with higher precision and accuracy than those of RTK-GNSS and 100 Hz sampling frequency. In the proposed method, a heuristic drift reduction method estimates displacement with better accuracy employing a low-pass-filtered acceleration measurement by an accelerometer and a displacement measurement by an RTK-GNSS receiver. Then, the displacement estimated by the heuristic drift reduction method, the velocity measured by a single GNSS receiver, and the acceleration measured by the accelerometer are combined in a two-stage Kalman filter to estimate the dynamic displacement. The effectiveness of the proposed dynamic displacement estimation method was validated through three field application tests at Yeongjong Grand Bridge in Korea, San Francisco–Oakland Bay Bridge in California, and Qingfeng Bridge in China. In the field tests, the root-mean-square error of RTK-GNSS displacement measurement reduces by 55–78 percent after applying the proposed method.

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“…With the plane displacement measurement accurate to the submillimeter, DGPS technology has become a mature measurement method in geodetic surveying and a standard method for monitoring the movement of bridge structures [8]. However, this technique has poor accuracy in vertical displacement measurements and cannot meet the accuracy requirements of bridge dynamic displacement measurements [9].…”

Section: Introductionmentioning

confidence: 99%

Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition

Zou

Chen

Liu

2019

Sensors

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Considering the lack of precision in transforming measured micro-electro-mechanical system (MEMS) accelerometer output signals into elevation signals, this paper proposes a bridge dynamic displacement reconstruction method based on the combination of ensemble empirical mode decomposition (EEMD) and time domain integration, according to the vibration signal traits of a bridge. Through simulating bridge analog signals and verifying a vibration test bench, four bridge dynamic displacement monitoring methods were analyzed and compared. The proposed method can effectively eliminate the influence of low-frequency integral drift and high-frequency ambient noise on the integration process. Furthermore, this algorithm has better adaptability and robustness. The effectiveness of the method was verified by field experiments on highway elevated bridges.

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Structural displacement estimation through multi-rate fusion of accelerometer and RTK-GPS displacement and velocity measurements

Cited by 59 publications

References 42 publications

Global Navigation Satellite System‐based positioning technology for structural health monitoring: a review

Global Navigation Satellite System‐based positioning technology for structural health monitoring: a review

Dynamic Displacement Estimation for Long-Span Bridges Using Acceleration and Heuristically Enhanced Displacement Measurements of Real-Time Kinematic Global Navigation System

Refactoring and Optimization of Bridge Dynamic Displacement Based on Ensemble Empirical Mode Decomposition

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