A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory

Abstract

The accuracy and integrity of structural deformation monitoring can be improved by the GNSS/accelerometer integrated system, and gross error detection is the key to further improving the reliability of GNSS/accelerometer monitoring. Traditional gross error detection methods assume that real-state information is known, and they need to establish state iterators, which leads to low computational efficiency. Meanwhile, in multi-sensor fusion, if the sampling rates are different, the change in the dimension of the observation matrix must be considered, and the calculation is complex. Based on state-domain consistency theory, this paper proposes the State-domain Robust Autonomous Integrity Monitoring by Extrapolation (SRAIME) method for identifying slow-growing gross errors for GNSS/accelerometer integrated deformation monitoring. Compared with the traditional gross error detection method, the proposed method constructs state test statistics based on the state estimated value and the state predicted value, and it directly performs gross error identification in the state domain. This paper deduces the feasibility of the proposed method theoretically and verifies that the proposed method does not need to consider the dimension change of the observation matrix in gross error detection. Meanwhile, in the excitation deformation experiments of the Suntuan River Bridge in Anhui and the Wilford Bridge in the United Kingdom, the slow gradient of the slope was added to the measurement domain, and the traditional AIME method and the method proposed in this paper were adopted for the gross error identification of the GNSS/accelerometer fusion process. The results demonstrate that both methods can effectively detect gross errors, but the proposed method does not need to consider the dimensional change in the observation matrix during the fusion process, which has better applicability to GNSS/accelerometer integrated deformation monitoring.

A Computationally Efficient Global Indicator to Detect Spurious Measurement Drifts in Kalman Filtering

“…These types of errors occur frequently when using a GPS. They are known in the related literature as slowly growing errors (SGE), see e.g., [4,7,8,5,18,14]. This is illustrated on Figure 2.…”

“…These types of faults are more difficult to detect than outliers because they do not correspond to an abrupt change in the signal. Additive ramps on the outputs or "slowlygrowing errors" are a typical failure of the Gobal Positioning System (GPS), see [7,8,4,5], with recent further developments in [18,14], and arise also in process control [10].…”

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

“…The feasibility of this method was assessed through simulation experiments and the analysis of the measured data of Wilford Bridge in England [28]. At the same time, the world's first highly integrated GNSS/accelerometer receiver for bridge monitoring has been developed and used for monitoring the FRB in Scotland [29].…”

“…The caveat is that outliers may be caused by real structural damages or simply due to sensor anomaly. Su et al (2022) proposed a state-domain robust autonomous integrity monitoring algorithm based on extrapolation to identify slow-growing gross errors of GNSS/accelerometers in a BSHM system [29]. Whereas this method can detect gross errors, it cannot be used to eliminate gross errors.…”