Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring
Abstract
:1. Introduction
2. The Principle of the VMD Algorithm
2.1. The Derivation of the VMD Algorithm
2.2. The Process of the VMD Algorithm
- For each mode function , the marginal spectrum is obtained by Hilbert transform;
- Using exponential correction, the frequency spectrum of modal function is moved to the center frequency of each estimation;
- The signal is demodulated by Gaussian smoothing (the square root of norm gradient) to obtain the bandwidth of each modal function.
2.3. The VMD Algorithm to Decompose the GNSS Time Series (Algorithm 1)
Algorithm 1: The VMD algorithm to decompose the GNSS time series |
Initialize, where is the iteration number. |
repeat the entire cycle, . |
For do Update for all , by Equation (8); Update , by Equation (10); end for |
Do dual ascent for all , by Equation (9); |
until Iterative constraints satisfied: |
3. Integrating the Displacement of GNSS and Speedometer
3.1. Decompose the GNSS Displacement Time Series by the VMD
- Low-frequency trend: this component mainly consists of the low-frequency displacement of the bridge and the multi-path effect of GNSS (caused by the span wires and the vehicles); after the multi-path is weakened by the appropriate algorithm, this component can reflect the low-frequency displacement of the bridge accurately.
- Vibration signal: this component reflects the vibration of the bridge structure, and the frequency is generally between 0.5 and 5 Hz; the amplitude is several centimeters or millimeters, which can be used to judge the health of the bridge.
- High-frequency noise: this component has the highest frequency among all the three components, which is caused by the receiver and GNSS technology itself, and cannot be eliminated directly during the positioning solution, which is generally up to mm level.
3.2. Data Fusion of GNSS Low-Frequency Trend and Speedometer Displacement
- The unity of sampling rate
- The unity of coordinate system
- The unity of time
Algorithm 2: Evaluate the Accuracy of Time Synchronization |
Get GNSS low-frequency trend by the VMD, Get speedometer displacement by integral(Time period of is included in ); |
Initialize: , , , ; |
For do If ; End if End for |
Return, ; |
4. Results
4.1. The Results of Simulation Data
4.1.1. Signal Without Noise
4.1.2. Signal with Noise
4.1.3. Discontinuous Signal
4.2. The Results of Measured Data
4.3. Algorithm Applied in Jiangyin Bridge
GNSS Time Series Analysis and Low-Frequency Displacement Extraction With the VMD
5. Discussion
- 1)
- The VMD algorithm used in this paper can effectively resist the modal aliasing phenomenon in the decomposition process caused by noise and discontinuous signals compared with EMD.
- 2)
- By a time series analysis and spectrum analysis on the decomposed signal, it is found that the VMD algorithm can extract the low-frequency trend term in the GNSS time series with high precision.
- 3)
- The data fusion algorithm proposed in this paper can combine the advantages of two sensors, GNSS and speedometer, and obtains high accuracy displacement including the low-frequency displacement and high-frequency vibration information of bridges.
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Xiong, C.; Lu, H.; Zhu, J. Operational modal analysis of bridge structures with data from GNSS/accelerometer measurements. Sensors 2017, 17, 436. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Afifi, A.; El-Rabbany, A. Precise point positioning using triple GNSS constellations in various modes. Sensors 2016, 16, 779. [Google Scholar] [CrossRef] [PubMed]
- Han, H.; Wang, J.; Meng, X.; Liu, H. Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading. Eng. Struct. 2016, 122, 238–250. [Google Scholar] [CrossRef]
- Kaloop, M.R.; Li, H. Sensitivity and analysis GPS signals based bridge damage using GPS observations and wavelet transform. Measurement 2011, 44, 927–937. [Google Scholar] [CrossRef]
- Lepadatu, A.; Tiberius, C. GPS for structural health monitoring–case study on the Basarab overpass cable-stayed bridge. J. Appl. Geod. 2014, 8, 65–86. [Google Scholar] [CrossRef] [Green Version]
- Norgard, P. Deformation Survey of the Storebaelt Bridge: GPS Shows Its Merits. GeornaticsInfo 1996, 10, 37–39. [Google Scholar]
- Meng, X.; Dodson, A.; Roberts, G.; Cosser, E. Hybrid Sensor System for Bridge Deformation Monitoring: Interfacing with Structural Engineers. In A Window on the Future of Geodesy; Springer: Berlin/Heidelberg, Germany, 2005. [Google Scholar]
- Li, X.; Ge, L.; Ambikairajah, E.; Rizos, C.; Tamura, Y.; Yoshida, A. Full-scale structural monitoring using an integrated GPS and accelerometer system. Gps Solut. 2006, 10, 233–247. [Google Scholar] [CrossRef]
- Yu, J.; Meng, X.; Shao, X.; Yan, B.; Yang, L. Identification of dynamic displacements and modal frequencies of a medium-span suspension bridge using multimode GNSS processing. Eng. Struct. 2014, 81, 432–443. [Google Scholar] [CrossRef]
- Yu, J.; Shao, X.; Meng, X. Dynamic Monitoring of Bridge Structures Combined with GNSS and Accelerometers. China J. Highw. Transp. 2014, 27, 62–69. [Google Scholar]
- Koo, G.; Kim, K.; Chung, J.; Choi, J.; Kwon, N.Y.; Kang, D.Y.; Sohn, H. Development of a High Precision Displacement Measurement System by Fusing a Low Cost RTK-GPS Sensor and a Force Feedback Accelerometer for Infrastructure Monitoring. Sensors 2017, 17, 2745. [Google Scholar] [CrossRef] [Green Version]
- Huang, N.E. The empirical mode decomposition and Hilbert spectrum for nonlinear and Hilbert spectrum for nonlinear and nonstationary time series analysis. Pro. R. Soc. 1998, 903–995. [Google Scholar] [CrossRef]
- Shen, N.; Chen, L.; Liu, J.; Wang, L.; Tao, T.; Wu, D.; Chen, R. A Review of Global Navigation Satellite System (GNSS)-Based Dynamic Monitoring Technologies for Structural Health Monitoring. Remote Sens. 2019, 11, 1001. [Google Scholar] [CrossRef] [Green Version]
- Chan, W.; Xu, Y.; Ding, X.; Dai, W. An integrated GPS–accelerometer data processing technique for structural deformation monitoring. J. Geod. 2006, 80, 705–719. [Google Scholar] [CrossRef]
- Ke, L. Denoising GPS-Based Structure Monitoring Data Using Hybrid EMD and Wavelet Packet. Math. Probl. Eng. 2017, 2017, 4920809. [Google Scholar] [CrossRef] [Green Version]
- Chao, L.; Feng, Z.; Yan, L. GPS/Pseudolites technology based on EMD-wavelet in the complex field conditions of mine. Procedia Earth Planet. Sci. 2009, 1, 1293–1300. [Google Scholar] [CrossRef] [Green Version]
- Dragomiretskiy, K.; Zosso, D. Variational mode decomposition. IEEE Trans. Signal Process. 2014, 62, 531–544. [Google Scholar] [CrossRef]
- Jegadeeshwaran, R.; Sugumaran, V.; Soman, K.P. Vibration based fault diagnosis of a hydraulic brake system using Variational Mode Decomposition (VMD). SDHM Struct. Durab. Health Monit. 2014, 10, 81–97. [Google Scholar]
- An, X.; Yang, J. Denoising of hydropower unit vibration signal based on variational mode decomposition and approximate entropy. Trans. Inst. Meas. Control 2016, 38, 282–292. [Google Scholar] [CrossRef]
- Sivavaraprasad, G.; Padmaja, R.S.; Ratnam, D.V. Mitigation of ionospheric scintillation effects on GNSS signals using variational mode decomposition. IEEE Geosci. Remote Sens. Lett. 2017, 14, 389–393. [Google Scholar] [CrossRef]
- Hu, A.; Sun, J.; Xiang, L. Modal aliasing problem in empirical mode decomposition. J. Vib. Test. Diagn. 2011, 31, 429–434. [Google Scholar]
- Huang, N.E.; Wu, Z. A review on Hilbert-Huang transform: Method and its applications to geophysical studies. Rev. Geophys. 2008, 46. [Google Scholar] [CrossRef] [Green Version]
- Sahmoudi, M.; Landry, R.; Kouki, A. A new approach for mitigating carrier phase multipath errors in multi-gnss real-time kinematic (RTK) receivers[C]. In Proceedings of the IEEE International Conference on Acoustics Speech & Signal Processing, Dallas, TX, USA, 14–19 March 2010; IEEE: Piscataway, NJ, USA, 2010. [Google Scholar]
- Dai, W.; Zhu, J.J.; Ding, X.L. Single epoch Ambiguity Resolution in Structure Monitoring Using GPS. Geomat. Indormation Sci. Wuhan Univ. 2007, 32, 234–237. [Google Scholar]
- Xin, J.; Zhou, J.; Yang, S.; Li, X.; Wang, Y. Bridge structure deformation prediction based on GNSS data using Kalman-ARIMA-GARCH model. Sensors 2018, 18, 298. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Zhao, S.; Cui, X.; Guan, F.; Lu, M. A Kalman filter-based short baseline RTK algorithm for single-frequency combination of GPS and BDS. Sensors 2014, 14, 15415–15433. [Google Scholar] [CrossRef]
Date | Equipment | Sampling Rate | Remark |
---|---|---|---|
2019-10-08 | Trimble BD990 | 10 Hz | Using a measuring antenna as Figure shown |
Magnetoelectric vibration sensor (DH610V) | 100 Hz | Timing with GNSS pps |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhang, R.; Gao, C.; Pan, S.; Shang, R. Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring. Sensors 2020, 20, 694. https://doi.org/10.3390/s20030694
Zhang R, Gao C, Pan S, Shang R. Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring. Sensors. 2020; 20(3):694. https://doi.org/10.3390/s20030694
Chicago/Turabian StyleZhang, Ruicheng, Chengfa Gao, Shuguo Pan, and Rui Shang. 2020. "Fusion of GNSS and Speedometer Based on VMD and Its Application in Bridge Deformation Monitoring" Sensors 20, no. 3: 694. https://doi.org/10.3390/s20030694