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Dynamic displacement estimation by fusing biased high-sampling rate acceleration and low-sampling rate displacement measurements using two-stage Kalman estimator
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  • Journal title : Smart Structures and Systems
  • Volume 17, Issue 4,  2016, pp.647-667
  • Publisher : Techno-Press
  • DOI : 10.12989/sss.2016.17.4.647
 Title & Authors
Dynamic displacement estimation by fusing biased high-sampling rate acceleration and low-sampling rate displacement measurements using two-stage Kalman estimator
Kim, Kiyoung; Choi, Jaemook; Koo, Gunhee; Sohn, Hoon;
 Abstract
In this paper, dynamic displacement is estimated with high accuracy by blending high-sampling rate acceleration data with low-sampling rate displacement measurement using a two-stage Kalman estimator. In Stage 1, the two-stage Kalman estimator first approximates dynamic displacement. Then, the estimator in Stage 2 estimates a bias with high accuracy and refines the displacement estimate from Stage 1. In the previous Kalman filter based displacement techniques, the estimation accuracy can deteriorate due to (1) the discontinuities produced when the estimate is adjusted by displacement measurement and (2) slow convergence at the beginning of estimation. To resolve these drawbacks, the previous techniques adopt smoothing techniques, which involve additional future measurements in the estimation. However, the smoothing techniques require more computational time and resources and hamper real-time estimation. The proposed technique addresses the drawbacks of the previous techniques without smoothing. The performance of the proposed technique is verified under various dynamic loading, sampling rate and noise level conditions via a series of numerical simulations and experiments. Its performance is also compared with those of the existing Kalman filter based techniques.
 Keywords
dynamic displacement;two-stage Kalman estimator;multi-rate data fusion;
 Language
English
 Cited by
1.
국산 고정밀 가속도계의 건설 구조물 적용성 평가,권남열;강두영;손훈;

한국전산구조공학회논문집, 2016. vol.29. 3, pp.277-283 crossref(new window)
1.
Application of High-precision Accelerometer Made in Korea to Health Monitoring of Civil Infrastructures, Journal of the Computational Structural Engineering Institute of Korea, 2016, 29, 3, 277  crossref(new windwow)
2.
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, 12, 2745  crossref(new windwow)
 References
1.
Boore, D.M. (2001), "Effect of baseline corrections on displacement and response spectra for several recordings of the 1999 Chi-Chi, Taiwan, earthquake", B. Seismol. Soc. Am., 91(5), 1199-1211.

2.
Boore, D.M., Stephens, C.D. and Joyner, W.B. (2002), "Comments on baseline correction of digital strong-motion data: examples from the 1999 Hector Mine, California, earthquake", B. Seismol. Soc. Am., 92(4), 1543-1560. crossref(new window)

3.
Cao, L. and Schwarz, H.M. (2003), "Exponential convergence of the Kalman filter based parameter estimation algorithm", Int. J. Adapt. Control, 17(10), 763-783. crossref(new window)

4.
Chan, W.S., Xu, Y.L., Ding, X.L. and Dai, W.J. (2006), "An integrated GPS-accelerometer data processing technique for structural deformation monitoring", J. Geodesy, 80(12), 705-719. crossref(new window)

5.
Chiu, H.C. (1997), "Stable baseline correction of digital strong-motion data", B. Seismol. Soc. Am., 87(4), 932-944.

6.
Cho, S., Yun, C.B. and Sim, S.H. (2015), "Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model", Smart Struct. Syst., 15(3), 645-663. crossref(new window)

7.
Esposito, S., Iervolino, I., d'Onofrio, A. and Santo, A. (2014), "Simulation-based seismic risk assessment of gas distribution networks", Comput.-Aided Civ. Inf., doi: 10.1111/mice.12105. crossref(new window)

8.
Faruqi, F.A. and Turner, K.J. (2000), "Extended Kalman filter synthesis for integrated global positioning / inertial navigation systems", Appl. Math. Comput., 115(2-3), 213-227. crossref(new window)

9.
Gindy, M., Vaccaro, R. Nassif, H. and Velde, J. (2008), "A state-space approach for deriving bridge displacement from acceleration", Comput.-Aided Civ. Inf., 23(4), 281-290. crossref(new window)

10.
He, W., Wu., Zhishen, Kojima, Y. and Asakura, T. (2009), Failure mechanism of deformed concrete tunnels subject to diagonally concentrated load, Comput.-Aided Civ. Inf., 24(6), 416-431. crossref(new window)

11.
Hong, S., Lee, M., Rios, J. and Speyer, J.L. (2000), "Observability analysis of GPS aided INS", Proceedings of the 13th International Technical meeting of the Satellite Division of the Institute of Navigation (ION GPS 2000), Sep. 19-22, 2000, Salt Lake City, UT.

12.
Hong, Y.H., Kim, H. and Lee, H.S. (2013), "Design of the FEM-FIR filter for displacement reconstruction using accelerations and displacements measured at different sampling rates", Mech. Syst. Signal Pr., 38(2), 460-481. crossref(new window)

13.
Jiang, X. and Adeli, H. (2005), "Dynamic wavelet neural network for nonlinear identification of highrise buildings", Comput.-Aided Civ. Inf., 20(5), 316-330. crossref(new window)

14.
Jo, H., Sim, S.H., Tatkowski, A., Spencer, Jr., B.F. and Nelson, M.E. (2013), "Feasibility of displacement monitoring using low-cost GPS receivers", Struct. Control Health Monit., 20(9), 1240-1254. crossref(new window)

15.
Kalman, R.E. (1960), "A new approach to linear filtering and prediction problems", J. Basic Eng., 82(1), 35-45. crossref(new window)

16.
Kim, J., Kim, K. and Sohn, H. (2013a), "Data-driven physical parameter estimation for lumped mass structures from a single point actuation test", J. Sound Vib., 332(18), 4390-4402. crossref(new window)

17.
Kim, J., Kim, K. and Sohn, H. (2013b), "In situ measurement of structural mass, stiffness, and damping using a reaction force actuator and a laser Doppler vibrometer", Smart Mater. Struct., 22(8), 085004. crossref(new window)

18.
Kim, J., Kim, K. and Sohn, H. (2014), "Autonomous dynamic displacement estimation from data fusion of acceleration and intermittent displacement measurements", Mech. Syst. Signal Pr., 42(1-2), 194-205. crossref(new window)

19.
Kim, S.W. and Kim, N.S. (2011), "Multi-point displacement response measurement of civil infrastructures using digital image processing", Procedia Eng., 14, 195-203. crossref(new window)

20.
Li, J., Hao, H., Fan, K. and Brownjohn, J. (2014), "Development and application of a relative displacement sensor for structural health monitoring of composite bridges", Struct. Control Health Monit., DOI: 10.1002/stc.1714. crossref(new window)

21.
Moore, J.B. (1973), "Discrete-time fixed-lag smoothing algorithms", Automatica, 9(2), 163-173. crossref(new window)

22.
Moschas, F. and Stiros, S. (2011), "Measurement of dynamic displacements and of the modal frequencies of a short-span pedestrian bridge using GPS and an accelerometer", Eng. Struct., 33(1), 10-17. crossref(new window)

23.
Park, H.S., Lee, H.M., Adeli, H. and Lee, I. (2007), "A new approach for health monitoring of structures: terrestrial laser scanning", Comput.-Aided Civ. Inf., 22(1), 19-30. crossref(new window)

24.
Park, H.S., Son, S., Choi, S.W. and Kim, Y. (2013), "Wireless laser range finder system for vertical displacement monitoring of mega-trusses during construction", Sensors, 13(5), 5796-5813. crossref(new window)

25.
Park, J.W., Sim, S.H. and Jung, H.J. (2013), "Displacement estimation using multimetric data fusion", IEEE/ASME T. Mechatronics, 18(6), 1675-1682. crossref(new window)

26.
Park, K.T., Kim, S.H., Park, H.S. and Lee, K.W. (2005), "The determination of bridge displacement using measured acceleration", Eng. Struct., 27(3), 371-378. crossref(new window)

27.
Rauch, H.E. (1963), "Solutions to the linear smoothing problem", IEEE T. Automat Contr., 8(4), 371-372. crossref(new window)

28.
Ruiz-Sandoval, M.E. and Morales, E. (2013), "Complete decentralized displacement control algorithm", Smart Struct. Syst., 11(2), 163-183. crossref(new window)

29.
Shin, S., Lee, S.U. and Kim, N.S. (2012), "Estimation of bridge displacement responses using FBG sensors and theoretical mode shapes", Struct. Eng. Mech., 42(2), 229-245. crossref(new window)

30.
Simon, D. (2006), Optimal state estimation-Kalman, $H{\infty}$, and nonlinear approaches, John Wiley & Sons Inc., Hoboken, NJ.

31.
Smyth, A. and Wu, M. (2007), "Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring", Mech. Syst. Signal Pr., 21(2), 706-723. crossref(new window)

32.
Tamura, Y., Matsui, M., Pagnini, L.C., Ishibashi, R. and Yoshida. A. (2002), "Measurement of wind-induced response of buildings using RTK-GPS", J. Wind Eng. Ind. Aerod., 90(12-15), 1783-1793. crossref(new window)

33.
Trifunac, M.D. (1971), "Zero baseline correction of strong motion accelerograms", B. Seismol. Soc. Am., 61(5), 1201-1211.

34.
Wang, N., O'Malley, C., Ellingwood, B.R. and Zureick, A.H. (2011), "Bridge rating using system reliability assessment. I: Assessment and verificiation by load testing", J. Bridge Eng., 16(6), 854-862. crossref(new window)

35.
Yang. H., Takaki, T. and Ishii, I. (2012), "Real-time multidirectional modal parameter estimation of beam-shaped objects using high-speed stereo vision", Proceedings of IEEE, Sensors, Taipei, Taiwan.

36.
Yun, X., Calusdian, J., Bachmann, E.R. and McGhee, R.B. (2012), "Estimation of human foot motion during normal walking using inertial and magnetic sensor measurements", IEEE T. Instrum. Meas., 61(7), 2059-2072. crossref(new window)

37.
Zhou, C., Li, H., Li, D., Lin, Y. and Yi, T. (2013), "Online damage detection using pair cointegration method of time-varying displacement", Smart Struct. Syst., 12(3-4), 309-325. crossref(new window)

38.
Zhu, L. (2003), "Recovering permanent displacements from seismic records of the June 9, 1994 Bolivia deep earthquake", Geophys. Res. Lett., 30(14), doi:10.1029/2003GL017302, 14. crossref(new window)