Optimal Fault Detection and Exclusion Applied in GNSS Positioning

Yang, Ling; Knight, Nathan L.; Li, Yong; Rizos, Chris

doi:10.1017/s0373463313000155

Cited by 17 publications

(10 citation statements)

References 13 publications

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“…To analyse the performance of the residual chi-square detection method, the concept of a minimal detectable bias (MDB) is introduced. The MDB is defined as the model error that can just be detected with the detection method (Yang et al, 2013; Teunissen, 2017).…”

Section: Residual Chi-square Detection Methodsmentioning

confidence: 99%

Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration

et al. 2020

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Real-time and accurate fault detection and isolation is very important to ensure the reliability and precision of integrated inertial navigation and global navigation satellite systems. In this paper, the detection performance of a residual chi-square method is analysed, and on this basis an improved method of fault detection is proposed. The local test based on a standardised residual is introduced to detect and identify faulty measurements directly. Differing from the traditional method, two appropriate thresholds are selected to calculate the weight factor of each measurement, and the gain matrix is adjusted adaptively to reduce the influence of the undetected faulty measurement. The sliding window test, which uses past measurements, is also added to further improve the fault detection performance for small faults when the local test based on current measurements cannot judge whether a fault has occurred or not. Several simulations are conducted to evaluate the proposed method. The results show that the improved method has better fault detection performance than the traditional detection method, especially for small faults, and can improve the reliability and precision of the navigation system effectively.

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Section: Residual Chi-square Detection Methodsmentioning

confidence: 99%

Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration

et al. 2020

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“…Combining the expressions of˜ and , it can be obtained that ≈ is established. From (17) and (18), it can be seen that the difference betweenX k and X k is related to the one step prediction of state estimationX k,k−1 , X k,k−1 and covariance matrixP ki , P k . According to the matrix theory, the relation between P k andP ki can be expressed as…”

Section: Performance Comparison Of the Two Methodsmentioning

confidence: 99%

“…In GNSS/INS integration, isolating the faulty subsystem will force the integrated system into the pure INS model, which will result in the decrease of the filtering precision [16]. Currently, the commonly used fault isolation method is identifying and isolating the faulty measurement by conducting the local test [17], [18]. After the isolation of the faulty measurements, the other normal measurements will be used for the measurement update process of the filter.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fault Isolation and System Reconfiguration Method for GNSS/INS Integration

et al. 2020

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Appropriate fault isolation and system reconfiguration scheme is of great significance to ensure the reliability and precision of the tightly coupled global navigation satellite system/inertial navigation system (GNSS/INS). Currently, the commonly used methods include fault isolation method and fault adaptation method. However, the performance of them is not compared and analyzed fully in the existing literatures. In this paper, the principle of them is analyzed and the performance of them under different conditions is compared in theory firstly. On this basis, to improve the effectiveness and adaptability of fault detection and isolation, an adaptive fault isolation and system reconfiguration method is proposed. The radial basis function neural network (RBFNN) is used to predict the pseudo-GNSS measurement for the measurement reconfiguration. Besides, taking the variety of observation conditions into consideration, an adaptive adjustment criterion is introduced to realize the switch of fault isolation and measurement reconfiguration. The performance of the proposed method is verified and compared with the traditional methods by using the field test data. The results show that the fault isolation method can obtain higher filtering precision than the fault adaptation method in theory, compared with these two methods, the proposed method has better adaptability to the complex environment, and can improve the reliability and precision of the navigation system effectively.

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“…The above methods are mainly based on the predicted residual of Kalman filter. In geodetic surveying, the commonly used method is based on the least-squares residual (estimated residual) [26]. In order to apply the related methods of GNSS receiver autonomous integrity monitoring (RAIM) into GNSS/INS integration, Hewitson proposed an extended RAIM (eRAIM) through adopting least-squares principles for the state estimation in a Kalman filter [27].…”

Section: Introductionmentioning

confidence: 99%

The Influence of Satellite Configuration and Fault Duration Time on the Performance of Fault Detection in GNSS/INS Integration

Zhang

Zhao

Pang

et al. 2019

Sensors

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For the integration of global navigation satellite system (GNSS) and inertial navigation system (INS), real-time and accurate fault detection is essential to enhance the reliability and precision of the system. Among the existing methods, the residual chi-square detection is still widely used due to its good real-time performance and sensibility of fault detection. However, further investigation on the performance of fault detection for different observational conditions and fault models is still required. In this paper, the principle of chi-square detection based on the predicted residual and least-squares residual is analyzed and the equivalence between them is deduced. Then, choosing the chi-square detection based on the predicted residual as the research object, the influence of satellite configuration and fault duration time on the performance of fault detection is analyzed in theory. The influence of satellite configuration is analyzed from the number and geometry of visible satellites. Several numerical simulations are conducted to verify the theoretical analysis. The results show that, for a single-epoch fault, the location of faulty measurement and the geometry have little effect on the performance of fault detection, while the number of visible satellites has greater influence on the fault detection performance than the geometry. For a continuous fault, the fault detection performance will decrease with the increase of fault duration time when the value of the fault is near the minimal detectable bias (MDB), and faults occurring on different satellite’s measurement will result in different detection results.

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Optimal Fault Detection and Exclusion Applied in GNSS Positioning

Cited by 17 publications

References 13 publications

Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration

Improved Fault Detection Method Based on Robust Estimation and Sliding Window Test for INS/GNSS Integration

Adaptive Fault Isolation and System Reconfiguration Method for GNSS/INS Integration

The Influence of Satellite Configuration and Fault Duration Time on the Performance of Fault Detection in GNSS/INS Integration

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