Real-time outlier detection in integrated GNSS and accelerometer structural health monitoring systems based on a robust multi-rate Kalman filter

Qu, Xuanyu; Ding, Xiaoli; Xu, You‐Lin; Yu, Weiping

doi:10.1007/s00190-023-01724-2

Cited by 11 publications

(3 citation statements)

References 49 publications

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“…To verify the characteristics of the proposed method in the frequency domain, the displacement power spectrum density (PSD) of the KF and KF + ANUE methods were calculated using the Welch algorithm [53], as shown in Figure 3b. In the low-frequency region below 1 Hz (Zone-1 and Zone-2), the PSD of KF and KF + ANUE is relatively consistent with the PSD of GNSS and shake table reference, indicating fewer errors in these frequency bands.…”

Section: Shake Table Simulation Experimentsmentioning

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

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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.

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Section: Shake Table Simulation Experimentsmentioning

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

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“…Other researchers have combined GPS low-frequency displacements with highfrequency accelerometer displacements after denoising both GPS and accelerometer data, resulting in more accurate overall displacement measurements [18,19]. Furthermore, some researchers have developed multi-rate Kalman filters to merge GNSS and accelerometer data for high-precision dynamic displacement estimation [20,21]. In GNSS/accelerometer integrated monitoring, accelerometer data usually contain baseline drift errors, primarily caused by ground tilting [22].…”

Section: Introductionmentioning

confidence: 99%

Accelerometer static state detection (SSD)-assisted GNSS/accelerometer bridge monitoring algorithm

Yang,

Li,

et al. 2024

Meas. Sci. Technol.

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In the field of structural health monitoring, a loosely coupled (LC) Kalman filtering algorithm that accounts for baseline drift errors is commonly used to integrate Global Satellite Navigation and Positioning System (GNSS) data with accelerometer data. In the LC algorithm, the baseline drift errors are considered unknown parameters that need to be estimated. In scenario of continuous float solutions, the estimation of baseline drift errors is often inaccurate, leading to the divergence of monitoring results. Theoretically, as a type of motion sensor, accelerometers are expected to qualitatively determine the priori state of bridges, whether dynamic or static. Utilizing the inherent characteristics of accelerometers and the principle of zero-velocity detection in integrated navigation, we originally propose a bridge static state detection (SSD) method based on low-cost accelerometer, and introduces this prior SSD information as a constraint in GNSS/accelerometer LC algorithm, called SSD-LC bridge monitoring algorithm. Through a simulation platform and real-world bridge monitored tests, the effectiveness of our proposed SSD method has been verified. Furthermore, our proposed SSD-LC bridge monitoring algorithm can effectively mitigate the divergence problem in baseline drift estimation that occurs with continuous GNSS float solutions in traditional algorithms, which can effectively avoid misjudgments and false alarms in bridge monitoring during GNSS anomalies.

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“…GNSS technology has been commonly integrated with other sensors considering the complementary natures of the sensors to improve the overall performance of the fusion systems [12,13]. For example, GNSS and accelerometers have been integrated in many cases [14,15] in both postprocessing mode using, e.g., empirical mode decomposition-(EMD-) based [16] or digital flter-based fusion method [17,18,19], and real-time mode using, e.g., multi-rate Kalman flter (MRKF) [20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Correction of Misalignment Errors in the Integrated GNSS and Accelerometer System for Structural Displacement Monitoring

Qu,

Ding,

et al. 2023

Structural Control and Health Monitoring

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Structural health monitoring (SHM) systems are widely deployed to monitor the dynamic behaviors of large civil infrastructures such as bridges and tall buildings. Global Navigation Satellite System- (GNSS-) based technologies are often a key component in such an SHM system considering the unique capability of GNSS in determining real-time displacements. GNSS often integrates with an accelerometer to achieve complementary advantages. However, due to the various error sources in GNSS measurements and accelerometer, accuracies of GNSS and accelerometer fusion results often cannot meet the requirements of SHM. We propose to integrate a multi-antenna GNSS and an accelerometer with an unscented multi-rate Kalman filter (UMRKF-MA) to correct the system misalignment errors between the sensors, aiming to produce a much more accurate real-time displacement measurement technology for monitoring large civil infrastructures. Extensive experiments with datasets gathered using a shaking table have indicated that the proposed method was able to improve the accuracy of real-time displacement measurements by up to about 40–65% compared to some existing approaches, and that a 1 mm level of real-time monitoring of displacements could be achieved with the method. The method has also been applied to process a dataset from a real-world long-span bridge when heavy vehicles passed through the bridge in a loading test and significantly improved results were obtained.

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Real-time outlier detection in integrated GNSS and accelerometer structural health monitoring systems based on a robust multi-rate Kalman filter

Cited by 11 publications

References 49 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

Accelerometer static state detection (SSD)-assisted GNSS/accelerometer bridge monitoring algorithm

Correction of Misalignment Errors in the Integrated GNSS and Accelerometer System for Structural Displacement Monitoring

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