Outlier Detection Based on Nelder-Mead Simplex Robust Kalman Filtering for Trustworthy Bridge Structural Health Monitoring

Hu, Liangliang; Bao, Yan; Sun, Zhe; Meng, Xiaolin; Tang, Chao; Zhang, Dongliang

doi:10.3390/rs15092385

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…Our innovative sensor was installed and is running well on the Forth Road Bridge in the UK and the Husutong Yangtze River Bridge, the Zhixi Yangtze River Bridge, etc., in China. To take full advantage of this sensor, some research was carried out by our team, such as using accelerometer data to detect GNSS gross errors and improve GNSS positioning accuracy [28]. It also should be pointed out that less research has been conducted on the stability of the low-cost MEMS accelerometer in an operational environment compared with the high-quality accelerometer.…”

Section: Discussionmentioning

confidence: 99%

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An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring

Xie,

Zhang,

Meng

et al. 2024

Remote Sensing

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This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer’s time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out.

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Section: Discussionmentioning

confidence: 99%

“…Low-cost MEMS accelerometers were adopted to improve the performance of GNSS landslide monitoring [27]. The error of GNSS data also can be detected by accelerometer data analysis [28].…”

Section: Introductionmentioning

confidence: 99%

An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring

Xie,

Zhang,

Meng

et al. 2024

Remote Sensing

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Enhanced robust multimode process monitoring under dirty data via difference-based decomposition of matrix

Wang,

Zheng,

et al. 2023

Journal of Process Control

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A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

Yin,

Yang,

et al. 2023

Remote Sensing

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Global Navigation Satellite Systems (GNSS) integrated with Inertial Navigation Systems (INS) have been widely applied in many Intelligent Transport Systems. However, due to the influence of various factors, such as complex urban environments, etc., accurately describing the measurement noise statistics of GNSS receivers and inertial sensors is difficult. An inaccurate definition of the measurement noise covariance matrix will lead to the rapid divergence of the position error of the integrated navigation system. To overcome this problem, this paper proposed a Robust Adaptive Extended Kalman Filter (RAKF) method based on an improved measurement noise covariance matrix. By analyzing and considering the position accuracy factors, measurement factor, and position standard deviation in GNSS measurement results, this paper constructed the optimal measurement noise covariance matrix. Based on the Huber model, this paper constructed a two-stage robust adaptive factor expression and obtained the robust adaptive factors with and without abnormal disturbances. And robust adaptive filtering was carried out. To assess the performance of this method, the author conducted experiments on land vehicles by using a self-developed POS system (GNSS/INS combined navigation system). The classic Extended Kalman Filter algorithm (EKF), Adaptive Kalman Filter (AKF) algorithm, Robust Kalman Filter (RKF) algorithm, and the proposed method were compared through data processing. Experimental results show that compared with the classical EKF, AKF, and RKF, the positioning accuracies of the proposed method were improved by 72.43%, 2.54%, and 47.82%, respectively, in the vehicle land experiment. In order to further evaluate the performance of this method, the vehicle data were subjected to different times and degrees of disturbance experiments. Experimental results show that compared with EKF, AKF, and RKF, the heading angle accuracy had obvious advantages, and its accuracy was improved by 34.65%, 31.53%, and 18.36%, respectively. Therefore, this method can effectively monitor and isolate disturbance and improve the robustness, reliability, accuracy, and stability of GNSS/INS integrated navigation systems in complex urban environments.

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Outlier Detection Based on Nelder-Mead Simplex Robust Kalman Filtering for Trustworthy Bridge Structural Health Monitoring

Cited by 5 publications

References 26 publications

An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring

An Innovative Sensor Integrated with GNSS and Accelerometer for Bridge Health Monitoring

Enhanced robust multimode process monitoring under dirty data via difference-based decomposition of matrix

A Robust Adaptive Extended Kalman Filter Based on an Improved Measurement Noise Covariance Matrix for the Monitoring and Isolation of Abnormal Disturbances in GNSS/INS Vehicle Navigation

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