Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility
The increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the uniq...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/8/11/690 |
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| author | Oguz Kagan Isik Ivan Petrunin Antonios Tsourdos |
| author_facet | Oguz Kagan Isik Ivan Petrunin Antonios Tsourdos |
| author_sort | Oguz Kagan Isik |
| collection | DOAJ |
| description | The increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by urban settings, such as frequent signal blockages, multipath reflections, and Non-Line-of-Sight (NLoS) receptions. This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. The results indicated that environment-specific models significantly enhanced both accuracy and system availability, particularly in challenging urban scenarios. The integration of environment recognition into the integrity monitoring framework allows the dynamic adaptation to varying environmental conditions, thus substantially improving the reliability and safety of UAV operations in urban air mobility applications. This research offers a novel protection level (PL) estimation method and a framework tailored to GNSS integrity monitoring for UAM, which enhances the availability with narrower PL bound gaps without yielding higher integrity risks. |
| format | Article |
| id | doaj-art-b03de923d49d4500b67e111e62d06a7e |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-b03de923d49d4500b67e111e62d06a7e2024-11-26T18:00:52ZengMDPI AGDrones2504-446X2024-11-0181169010.3390/drones8110690Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air MobilityOguz Kagan Isik0Ivan Petrunin1Antonios Tsourdos2School of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Bedford MK43 0AL, UKSchool of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Bedford MK43 0AL, UKSchool of Aerospace, Transport and Manufacturing (SATM), Cranfield University, Bedford MK43 0AL, UKThe increasing deployment of unmanned aerial vehicles (UAVs) in urban air mobility (UAM) necessitates robust Global Navigation Satellite System (GNSS) integrity monitoring that can adapt to the complexities of urban environments. The traditional integrity monitoring approaches struggle with the unique challenges posed by urban settings, such as frequent signal blockages, multipath reflections, and Non-Line-of-Sight (NLoS) receptions. This study introduces a novel machine learning-based GNSS integrity monitoring framework that incorporates environment recognition to create environment-specific error models. Using a comprehensive Hardware-in-the-Loop (HIL) simulation setup, extensive data were generated for suburban, urban, and urban canyon environments to train and validate the models. The proposed Natural Gradient Boosting Protection Level (NGB-PL) method, leveraging the uncertainty prediction capabilities of the NGB algorithm, demonstrated superior performance in estimating protection levels compared to the classical methods. The results indicated that environment-specific models significantly enhanced both accuracy and system availability, particularly in challenging urban scenarios. The integration of environment recognition into the integrity monitoring framework allows the dynamic adaptation to varying environmental conditions, thus substantially improving the reliability and safety of UAV operations in urban air mobility applications. This research offers a novel protection level (PL) estimation method and a framework tailored to GNSS integrity monitoring for UAM, which enhances the availability with narrower PL bound gaps without yielding higher integrity risks.https://www.mdpi.com/2504-446X/8/11/690GNSSnavigationUAMUAVprotection level |
| spellingShingle | Oguz Kagan Isik Ivan Petrunin Antonios Tsourdos Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility Drones GNSS navigation UAM UAV protection level |
| title | Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility |
| title_full | Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility |
| title_fullStr | Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility |
| title_full_unstemmed | Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility |
| title_short | Machine Learning-Based Environment-Aware GNSS Integrity Monitoring for Urban Air Mobility |
| title_sort | machine learning based environment aware gnss integrity monitoring for urban air mobility |
| topic | GNSS navigation UAM UAV protection level |
| url | https://www.mdpi.com/2504-446X/8/11/690 |
| work_keys_str_mv | AT oguzkaganisik machinelearningbasedenvironmentawaregnssintegritymonitoringforurbanairmobility AT ivanpetrunin machinelearningbasedenvironmentawaregnssintegritymonitoringforurbanairmobility AT antoniostsourdos machinelearningbasedenvironmentawaregnssintegritymonitoringforurbanairmobility |