Fault detection and exclusion for GNSS measurements using observations projection on information space

Mahmoud, Kaddour; Najjar, Maan E. El; Naja, Z.; Tmazirte, Nourdine Aït; Moubayed, Nazih

doi:10.1109/dictap.2015.7113199

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…In Kaddour et al (2015), the authors performed FDE by projecting the GNSS pseudoranges from the measurement space to the information space of a Kalman filter. This work performed fault exclusion by projecting the measurements into the position domain, and thereafter, clustering the ones most likely to be non-faulty.…”

Section: Fde Approaches For Gnss-only Setupmentioning

confidence: 99%

Robust GPS-Vision Localization via Integrity-Driven Landmark Attention

Bhamidipati

Gao

2022

navi

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For robust GPS-vision navigation in urban areas, we propose an integrity-driven landmark attention (ILA) technique via stochastic reachability. Inspired by cognitive attention in humans, we perform convex optimization to select a subset of landmarks from GPS and vision measurements that maximizes integrity-driven performance. Given known measurement error bounds in non-faulty conditions, our ILA technique follows a unified approach to address both GPS and vision faults and is compatible with any off-the-shelf estimator. We analyze measurement deviation to estimate the stochastic reachable set of positions associated with each landmark, which is parameterized via probabilistic zonotope (p-zonotope). We apply set union to formulate a p-zonotopic cost that represents the size of position bounds based on landmark inclusion/exclusion. We jointly minimize the p-zonotopic cost and maximize the number of landmarks via convex relaxation. For an urban data set, we demonstrate improved localization accuracy and robust predicted availability for a pre-defined risk and alert limit.

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Section: Fde Approaches For Gnss-only Setupmentioning

confidence: 99%

Robust GPS-Vision Localization via Integrity-Driven Landmark Attention

Bhamidipati

Gao

2022

navi

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“…Information space projection-based methods (Kaddour et al 2015) and recursive consistency check-based FDE (Blanch et al 2015) have been proposed to deal with multi-fault scenarios. These methods, however, demand a large amount of computing resources to enact, and have only been verified by simulated data.…”

Section: Introductionmentioning

confidence: 99%

A factor set-based GNSS fault detection and exclusion for vehicle navigation in urban environments

et al. 2023

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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 Measurement Units (IMU). The FDE is first performed efficiently via consistency checking over far fewer subsets of the pseudorange. Afterwards, the FDE results are validated by missed-detection and false-alarm checks. The misseddetection-check factor is designed by predicting the maximum horizontal GNSS positioning error, while the false-alarm-check factor is designed with the aid of IMU mechanization. Following FDE, a loosely coupled GNSS/IMU integration is carried out to output the final estimation of the position, velocity and attitude of the vehicle.The proposed algorithm improved both horizontal and 3D positioning accuracy by more than 50% in the field test, compared to the traditional GNSS/IMU loosely coupled scheme. Additionally, with the proposed algorithm, the resultant accuracy of the velocity and of the heading angle were improved by over 20% and 50% respectively.

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Section: Introductionmentioning

confidence: 99%

A Factor Set Based GNSS Fault Detection and Exclusion for Vehicle Navigation in Urban Environments

Chen

Sun

Cheng

et al. 2022

Preprint

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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 Measurement Units (IMU). The FDE is first performed efficiently via consistency checking over far fewer subsets of the pseudorange. Afterwards, the FDE results are validated by missed-detection and false-alarm checks. The missed-detection-check factor is designed by predicting the maximum horizontal GNSS positioning error, while the false-alarm-check factor is designed with the aid of IMU mechanization. Following FDE, a loosely coupled GNSS/IMU integration is carried out to output the final estimation of the position, velocity and attitude of the vehicle. The proposed algorithm improved both horizontal and 3D positioning accuracy by more than 50% in the field test, compared to the traditional GNSS/IMU loosely coupled scheme. Additionally, with the proposed algorithm, the resultant accuracy of the velocity and of the heading angle were improved by over 20% and 50% respectively.

show abstract

Fault detection and exclusion for GNSS measurements using observations projection on information space

Cited by 5 publications

References 9 publications

Robust GPS-Vision Localization via Integrity-Driven Landmark Attention

Robust GPS-Vision Localization via Integrity-Driven Landmark Attention

A factor set-based GNSS fault detection and exclusion for vehicle navigation in urban environments

A Factor Set Based GNSS Fault Detection and Exclusion for Vehicle Navigation in Urban Environments

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