Fault exclusion method for ARAIM based on tight GNSS/INS integration to achieve CAT‐I approach

Pan, Weichuan; Zhan, Xingqun; Zhang, Xin

doi:10.1049/iet-rsn.2019.0179

Cited by 16 publications

(11 citation statements)

References 25 publications

(40 reference statements)

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“…It not only provides a navigation solution for the crew, but also monitors the reliability of the navigation output through a series of augmentation techniques [1-6], such as receiver autonomous integrity monitoring (RAIM), which is achieved by a consistency check among redundant pseudo-range measurements [7][8][9]. The RAIM monitoring technique has been commonly implemented especially in aerial navigation and air transport area for keeping the safety of the flight [10][11][12][13][14][15], while also being investigated in the maritime navigation field [16][17][18]. The snapshot RAIM process only needs the satellite signals in current epoch for operating and requires no additional aid information beyond the receiver, thus has been widely investigated and employed for the last decades [19][20][21].…”

Section: Instructionmentioning

confidence: 99%

Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications

et al. 2021

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Satellite selection is an effective way to overcome the challenges for the processing capability and channel limitation of the receivers due to superabundant satellites in view. The satellite selection strategies have been widely investigated to construct the subset with high accuracy but deserve further studies when applied to safety-critical applications such as the receiver autonomous integrity monitoring (RAIM) technique. In this study, the impacts of subset size on the accuracy and integrity of the subset and computation load are analyzed at first to confirm the importance of the satellite selection strategy for the RAIM process. Then the integrated performance impact of a single satellite on the current subset is evaluated according to the performance requirement of the flight phase. Subsequently, a performance-requirement-driven fast satellite selection algorithm is proposed based on the impact evaluation to construct a relatively small subset that satisfies the accuracy and integrity requirements. Comparison simulations show that the proposed algorithm can keep similar accuracy and better integrity performances than the geometric algorithm and the downdate algorithm when the subset size is fixed to 12, and can achieve an average 1.0 to 2.0 satellites smaller subset in the Lateral Navigation (LNAV) and approach procedures with vertical guidance (APV-I) horizontal requirement trial. Thus, it is suitable for real-time RAIM applications and low-cost navigation devices.

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Section: Instructionmentioning

confidence: 99%

Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications

et al. 2021

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“…The state vector

{bold-italicx}_{k}

is a 17 × 1 matrix, including INS attitude errors, INS velocity errors, INS position errors, accelerometer biases, gyroscope biases, GNSS receiver clock offsets and drifts.

{bold-italicz}_{k}

is the measurement vector, which represents the difference between the pseudo range and pseudo range rate estimated by INS and provided by GNSS receiver [18].

{boldΦ}_{k / k - 1}

{boldΓ}_{k - 1}

and

{bold-italicH}_{k}

are the state transition matrix, process noise matrix and observation matrix, respectively.…”

Section: Dynamic Fading Filter Optimisation Under Fault‐free Conditionsmentioning

confidence: 99%

Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Wang

Zhu

et al. 2021

IET Radar Sonar & Navi

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In the application of integrity monitoring of Global Navigation Satellite System (GNSS)/Inertial Navigation System integrated navigation system in complex environments, such as vegetation covered areas, the Kalman filtering (KF) has the disadvantages of low positioning precision, poor performance of fault identification and error bounds. This study proposes an integrity monitoring method based on dynamic fading filter optimisation. According to the real‐time updates of filtering innovation, a fading filtering algorithm is designed, which can adjust the fading factor dynamically. The dynamic fading filtering algorithm is then combined with the fault identification algorithm, which integrates chi‐square and multiple solution separation. An optimisation strategy of setting a fading period to speed up the filter convergence is proposed. The simulation results show that compared with the KF, the dynamic fading filtering reduces the standard deviations of positioning errors by 26%, 39%, and 26%, respectively, in the east, north and up directions. By reducing the filter convergence period, the proposed dynamic fading filtering can enhance the accuracy of the identification results when the subsequent GNSS fault is minor. It can shorten the identification time to about 20% when the subsequent fault is large. The dynamic fading filtering can also limit the state errors below the bounds correctly.

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“…Because of the hostile environment and hardware damage, many types of faults of sensors will occur, including abrupt, gradual, oscillating [3,7,8], which may cause serious accidents [3]. Hence, the quick detection and isolation of these faults are highly desirable [9,10]. The Chi-square fault detection algorithm is mostly applied to integrated navigation [11].…”

Section: Introductionmentioning

confidence: 99%

Fault detection approach applied to inertial navigation system/air data system integrated navigation system with time‐offset

Chen

Wang

et al. 2021

IET Radar Sonar & Navi

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The false alarm of a fault detection module in aircraft will interfere with flight. If there is a time-offset between the inertial navigation system (INS) and air data system (ADS), the probability of a false alarm (PFA) of the fault detection module will increase, which is ignored in existing approaches. To address the problem, a fault detection approach is proposed applied to INS/ADS with a time-offset. An INS/ADS fault detection model based on kinematic equations is developed, and we combine an unscented Kalman filter (UKF) with Runge-Kutta to deal with the non-linear and discretisation problem. A timeoffset estimator is designed and the observability of time-offset is analysed, showing that time-offset is observable only in the manoeuvre phase. A fault detection architecture applied to INS/ADS with a time-offset is designed, which solves the problem of the high PFA of INS/ADS fault detection under a time-offset. Simulation results show that under multiple time-offset scenarios, the root mean square error of the proposed approach can reach 0.0134 s minimally, and after time alignment, the PFA of the fault detection can be reduced to 1.8%.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Fault exclusion method for ARAIM based on tight GNSS/INS integration to achieve CAT‐I approach

Cited by 16 publications

References 25 publications

Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications

Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications

Integrity monitoring of Global Navigation Satellite System/Inertial Navigation System integrated navigation system based on dynamic fading filter optimisation

Fault detection approach applied to inertial navigation system/air data system integrated navigation system with time‐offset

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