Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring

Smyth, Andrew W.; Wu, Meiliang

doi:10.1016/j.ymssp.2006.03.005

Cited by 316 publications

(220 citation statements)

References 5 publications

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“…Since the acceleration and displacement have different sampling rates, the optimal estimates of the state variables can process by multi-rate Kalman filter [4]. In time steps when displacement measurements are not available, only the prediction step is performed.…”

Section: Formulation Of the Kalman Filtermentioning

confidence: 99%

“…The large sampling interval of the laser sensor can lead to drift of the displacement estimation. Smoothing can produce a better assessment [4].…”

Section: Formulation Of the Kalman Filtermentioning

confidence: 99%

“…Due to sensors specific, the measurements of laser sensor and accelerometer are usually taken at different sampling rates. Smyth and Wu [4] used multi-rate Kalman filter data fusion to evaluate displacements. Measurement process was modelled using the state-space equation for velocity and acceleration.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic displacement estimation using data fusion

Upnere¹,

Jēkabsons²

2017

Vib. proced.

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Abstract. The paper describes a Kalman filtering technique for dynamic displacement estimation using accelerometer and laser sensor measurements. Data fusion of measurements from multiple sensors can give the more accurate results because of different advantages of sensors. Since the acceleration and displacement have different sampling rates, the multi-rate Kalman filter is applied. The filter is expanded with the fixed interval smoother to improve reconstruction accuracy of displacements. A modelled signal consisting of two sinus functions and Gaussian distributed noise is used to validate developed state-space model.

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Section: Formulation Of the Kalman Filtermentioning

confidence: 99%

“…The large sampling interval of the laser sensor can lead to drift of the displacement estimation. Smoothing can produce a better assessment [4].…”

Section: Formulation Of the Kalman Filtermentioning

confidence: 99%

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Dynamic displacement estimation using data fusion

Upnere¹,

Jēkabsons²

2017

Vib. proced.

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show abstract

“…Hence accelerometer and GPS signals linked e.g. by Kalman filter should be a good approach (Smyth & Wu, 2007). Neither extensometer system used here was totally satisfactory, so a solution with short range, high resolution lasers would be preferred, preferably at both ends of the span, at either side.…”

Section: Sensor Performancementioning

confidence: 99%

Operational deformations in long-span bridges

Brownjohn

Koo

Scullion

et al. 2014

Structure and Infrastructure Engineering

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“…Chan et al (2007) proposed an integrated GPS-accelerometer data processing technique based on the empirical mode decomposition (EMD) and adaptive filter techniques [31]. Smyth et al (2007) presented a multi-rate Kalman filtering approach to integrate GPS and accelerometer data at different rates [32], Hwang's research shows the frequency-based displacement extraction method was most appropriate for the GPS/accelerometer data integration [33]. However, a tightly coupled integration of GPS and accelerometer for online monitoring has not yet been developed.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading

Han

Wang

Meng

et al. 2016

Engineering Structures

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(2016) Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading. Engineering Structures, 122 (2016 A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading AbstractLarge flexible engineering structures, such as long span bridges or tall buildings, are susceptible to quasi-static and dynamic deformations caused by different loading situations, thus accurate displacement measurements are desirable to assess the integrity and reliability of the structure. In this study, an integrated system that includes Global Positioning System (GPS), accelerometer and anemometer was developed to obtain the responses of a long span bridge to the extreme wind loading. Spectral analysis based on the Fast Fourier Transform (FFT) algorithm was first carried out to detect the dominant frequencies of the bridge. Then the noisy GPS displacement measurements and accelerometer data are de-noised using the Vondrak filter, and the low frequency disturbance was separated from GPS displacement time series. A least-squares based displacement reconstruction scheme using noise-mitigated accelerations was employed, and the Tikhonov regularization scheme with optimal selected regularization factor was used to alleviate the ill-posedness. At last, an adaptive recursive least squares (RLS) filter was adopted to separate the slow-varying movements, and the total displacement with enhanced measurement accuracy was obtained from the combined quasistatic and high-frequency dynamic displacements. A field monitoring data set collected on the Erqi Yangtze River Bridge, a three-tower cable-stayed bridge located in Wuhan in China, was used to validate the effectiveness of the proposed integration processing scheme. The GPS/accelerometer/anemometer installed on the center supporting tower was used to characterize the interaction between the responses and the ambient wind loadings. The results demonstrate the proposed technique can significantly improve the measurement accuracy of bridge displacement under strong winds. The deformation with the amplitude of several millimeters can be successfully detected, and the spectrum of the bridge response obtained from both GPS data and accelerometer data reveals the identified first natural frequency of the bridge is 0.172 Hz.

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Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring

Cited by 316 publications

References 5 publications

Dynamic displacement estimation using data fusion

Dynamic displacement estimation using data fusion

Operational deformations in long-span bridges

Analysis of the dynamic response of a long span bridge using GPS/accelerometer/anemometer under typhoon loading

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