Integrity monitoring with MAIME upon integrated GNSS/SINS for SGEs

Zhu, Biao; Meng, Fanchen

doi:10.1109/compcomm.2017.8322809

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…These methods, however, demand a large amount of computing resources to enact, and have only been verified by simulated data. FDE based on innovation of GNSS/IMU fusion have also been applied to improve the performance of integrated navigation systems (Hwang et al 2005;Zhu et al 2017;Sun et al 2021).…”

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

et al. 2023

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A GNSS Quality Control Based GNSS/IMU Integrated Navigation Algorithm in Urban Environments

Chen

Sun

2022

Lecture Notes in Electrical Engineering

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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

Integrity monitoring with MAIME upon integrated GNSS/SINS for SGEs

Cited by 4 publications

References 10 publications

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

A GNSS Quality Control Based GNSS/IMU Integrated Navigation Algorithm in Urban Environments

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

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