Dynamic displacement monitoring by integrating high-rate GNSS and accelerometer: on the possibility of downsampling GNSS data at reference stations

Paziewski, Jacek; Stępniak, Katarzyna; Sieradzki, Rafal; Yiğit, Cemal Özer

doi:10.1007/s10291-023-01500-x

Cited by 9 publications

(2 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where K k is the gain matrix, Xk is the estimated state with the covariance matrix P k , I 6×6 stands for the 6 by 6 unit matrix. Due to the varying sampling rates of GNSS displacement and strong motion acceleration, the time updates of Equations ( 3) and (4) are performed upon each accelerometer sampling, while measurement updates of Equations ( 5)-( 7) are performed at each GNSS sampling [50].…”

Section: Traditional Kalman Filter Methodsmentioning

confidence: 99%

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

Zhang,

Nie,

Wang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

A strong motion seismometer is a kind of inertial sensor, and it can record middle- to high-frequency ground accelerations. The double-integration from acceleration to displacement amplifies errors caused by tilt, rotation, hysteresis, non-linear instrument response, and noise. This leads to long-period, non-physical baseline drifts in the integrated displacements. GNSS enables the direct observation of the ground displacements, with an accuracy of several millimeters to centimeters and a sample rate of 1 Hz to 50 Hz. Combining GNSS and a strong motion seismometer, one can obtain an accurate displacement series. Typically, a Kalman filter is adopted to integrate GNSS displacements and strong motion accelerations, using the empirical values of noise uncertainty. Considering that there are significantly different errors introduced by the above-mentioned tilt, rotation, hysteresis, and non-linear instrument response at different stations or at different times at the same station, it is inappropriate to employ a fixed noise uncertainty for strong motion accelerations. In this paper, we present a Sage–Husa Kalman filter, where the noise uncertainty of strong motion acceleration is adaptively estimated, to integrate GNSS and strong motion acceleration for obtaining the displacement series. The performance of the proposed method was validated by a shake table simulation experiment and the GNSS/strong motion co-located stations collected during the 2023 Mw 7.8 and Mw 7.6 earthquake doublet in southeast Turkey. The experimental results show that the proposed method enhances the adaptability to the variation of strong motion accelerometer noise level and improves the precision of integrated displacement series. The displacement derived from the proposed method was up to 28% more accurate than those from the Kalman filter in the shake table test, and the correlation coefficient with respect to the references arrived at 0.99. The application to the earthquake event shows that the proposed method can capture seismic waveforms at a promotion of 46% and 23% in the horizontal and vertical directions, respectively, compared with the results of the Kalman filter.

show abstract

Section: Traditional Kalman Filter Methodsmentioning

confidence: 99%

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

Zhang,

Nie,

Wang

et al. 2024

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…GNSS technology is extensively utilized in geodetic monitoring studies of tectonic movements [1][2][3], landslides [4][5][6][7][8], and a variety of natural events which require high accuracy observations. Another field where GNSS technology is heavily employed is in structural health monitoring studies [9][10][11][12]. GNSS measurement and processing methods exhibit variability in correspondence with evolving application domains.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Horizontal Displacements with Low-Cost GNSS Systems Using Relative Positioning: Performance Analysis

Akpınar,

Özarpacı

2024

Applied Sciences

View full text Add to dashboard Cite

Monitoring horizontal displacements, such as landslides and tectonic movements, holds great importance and high-cost geodetic GNSS equipment stands as a crucial tool for the precise determination of these displacements. As the utilization of low-cost GNSS systems continues to rise, there is a burgeoning interest in evaluating their efficacy in measuring such displacements. This evaluation is particularly vital as it explores the potential of these systems as alternatives to high-cost geodetic GNSS systems in similar applications, thereby contributing to their widespread adoption. In this study, we delve into the assessment of the potential of the dual-frequency U-Blox Zed-F9P GNSS system in conjunction with a calibrated survey antenna (AS-ANT2BCAL) for determining horizontal displacements. To simulate real-world scenarios, the Zeiss BRT 006 basis-reduktionstachymeter was employed as a simulation device, enabling the creation of horizontal displacements across nine different magnitudes, ranging from 2 mm to 50 mm in increments of 2, 4, 6, 8, 10, 20, 30, 40, and 50 mm. The accuracies of these simulated displacements were tested through low-cost GNSS observations conducted over a 24 h period in open-sky conditions. Additionally, variations in observation intervals, including 3, 6, 8, and 12 h intervals, were investigated, alongside the utilization of the relative positioning method. Throughout the testing phase, GNSS data were processed using the GAMIT/GLOBK GNSS (v10.7) software, renowned for its accuracy and reliability in geodetic applications. The insightful findings gleaned from these extensive tests shed light on the system’s capabilities, revealing crucial information regarding its minimum detectable displacements. Specifically, the results indicate that the minimum detectable displacements with the 3-sigma rule stand at 22.8 mm, 11.7 mm, 8.7 mm, and 4.8 mm for 3 h, 6 h, 8 h, and 12 h GNSS observations, respectively. Such findings are instrumental in comprehending the system’s performance under varying conditions, thereby informing decision-making processes and facilitating the adoption of suitable GNSS solutions for horizontal displacement monitoring tasks.

show abstract

High-rate bridge displacement monitoring with low-rate virtual reference station data

Qu,

Ding,

et al. 2024

GPS Solut

View full text Add to dashboard Cite

We present a new Global Navigation Satellite Systems (GNSS) positioning approach that utilizes low-rate Virtual Reference Station (VRS) data to achieve high-rate displacement monitoring. The method integrates tightly the VRS technology with asynchronous Real-Time Kinematic (RTK) method to overcome the limitation of VRS in high-rate structural health monitoring (SHM) applications. When this approach is used, no local reference station is required so that the efforts and cost of setting up reference stations can be avoided. Experiments with datasets from a controlled shaking platform and a long-span bridge in Hong Kong with both temperature and typhoon excitations have indicated that the proposed approach worked effectively. The results demonstrated that when a baseline exceeded about 3 km, the vertical errors of RTK GNSS positioning could be up to about 15.9 mm (standard deviations), insufficient for most SHM applications. In this case, the proposed method enhanced the accuracy by about 60% to 6.0 mm when using VRS data openly available in Hong Kong. The accuracy achieved was equivalent to that of RTK positioning using a 1.2 km baseline. The shaking platform trial demonstrated that the monitoring station could be up-sampled to 100 Hz without a noticeable loss in accuracy. The proposed method could capture precisely the peak frequencies and amplitudes of vibrations, with errors as low as 0.001 Hz and 0.1 mm. This method broadens the applicability of GNSS positioning in SHM applications.

show abstract

Dynamic displacement monitoring by integrating high-rate GNSS and accelerometer: on the possibility of downsampling GNSS data at reference stations

Cited by 9 publications

References 31 publications

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

Integration of High-Rate GNSS and Strong Motion Record Based on Sage–Husa Kalman Filter with Adaptive Estimation of Strong Motion Acceleration Noise Uncertainty

Monitoring Horizontal Displacements with Low-Cost GNSS Systems Using Relative Positioning: Performance Analysis

High-rate bridge displacement monitoring with low-rate virtual reference station data

Contact Info

Product

Resources

About