2023
DOI: 10.1007/s10291-023-01430-8
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A factor set-based GNSS fault detection and exclusion for vehicle navigation in urban environments

Abstract: With the rapid development of safety critical applications of Intelligent Transportation Systems (ITS), Global Navigation Satellite System (GNSS) fault detection and exclusion (FDE) methods have made navigation systems increasingly reliable.However, in multi-fault scenarios of urban environments, FDE methods generally demand massive calculations and have a high risk of missed detection and false alarm.To deal with this issue, we proposed a factor set based FDE algorithm for the integration of GNSS and Inertial… Show more

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Cited by 7 publications
(5 citation statements)
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“…We chose the larger of the two values to obtain the conservative inflation factors, which were 8.31 for HPF and 7.82 for LPF. Finally, the inflation fac- In this study, we demonstrate the computation of the inflation factor and resulting threshold to meet a false alarm requirement of 10 −5 , which has been used in various previous works [41,52,53]. We computed the terms from the training dataset and then applied them to the test dataset to verify the false alarm probability.…”
Section: Machine Learning Residual Estimationmentioning
confidence: 99%
“…We chose the larger of the two values to obtain the conservative inflation factors, which were 8.31 for HPF and 7.82 for LPF. Finally, the inflation fac- In this study, we demonstrate the computation of the inflation factor and resulting threshold to meet a false alarm requirement of 10 −5 , which has been used in various previous works [41,52,53]. We computed the terms from the training dataset and then applied them to the test dataset to verify the false alarm probability.…”
Section: Machine Learning Residual Estimationmentioning
confidence: 99%
“…In order to compute the PL, the maximum FMS corresponding to α 2 fe needs to be calculated for the effect of worst-case faults. For this purpose, fault contributions of each epoch's innovation term are required using equation (21) to form the necessary coefficient matrix in the denominator of the FMS. Let f fe, ℓ be the fault vector at time ℓ, and F full, ℓ−1 and K fe, ℓ be the state transition matrix and Kalman gain, respectively, of the full-order EKF.…”
Section: Skfmentioning
confidence: 99%
“…In order to avoid the architectural complexity with multiple parallel filters, KF integrity monitors in the range domain have been explored, where fault detection tests are designed with measurement innovations/residuals [15][16][17][18][19][20][21]. This method is called range-based integrity monitoring in this paper.…”
Section: Introductionmentioning
confidence: 99%
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“…Improvements in the GNSS mathematical models using artificial intelligence have significant advantages [1]. In fields such as autonomous vehicles, indoor and outdoor positioning, and intelligent transportation systems [2][3][4], artificial intelligence techniques have been used to improve the completeness, continuity, robustness, and accuracy of mathematical models for navigation and positioning. Before being combined with artificial intelligence, GNSS mathematical models (functional and stochastic) were used to achieve an optimal estimation of GNSS parameters [5,6].…”
Section: Introductionmentioning
confidence: 99%