Image-based conflict detection with convolutional neural network under weather uncertainty
Detection of air traffic conflicts in a weather constrained airspace is challenging given the inherent uncertainties and aircraft maneuvers which give rise to new conflict birth-points. Traditional conflict detection tools are untenable in such situations as they primarily rely on flight-plan, aircr...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/169380 |
| _version_ | 1826110290632114176 |
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| author | Dang, Phuoc Huu Mohamed Arif Bin Mohamed Alam, Sameer |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Dang, Phuoc Huu Mohamed Arif Bin Mohamed Alam, Sameer |
| author_sort | Dang, Phuoc Huu |
| collection | NTU |
| description | Detection of air traffic conflicts in a weather constrained airspace is challenging given the inherent uncertainties and aircraft maneuvers which give rise to new conflict birth-points. Traditional conflict detection tools are untenable in such situations as they primarily rely on flight-plan, aircraft performance characteristics and trajectories projection in short-term (2-4 minutes). This work adopts a convolutional neural network (CNN) model, on radar-like images, for conflict detection task in a constrained airspace. The CNN models are well-known for their learning capabilities when dealing with unstructured data like pixelated images. In this study, historical ADS-B data with weather constrained airspace is input as pixelated images to the CNN model. The learned model was compared with two well-known models for conflict detection (CD). The results demonstrated that the CNN based model was able to predict off-nominal conflict with high accuracy. The CNN model also demonstrated its ability to predict off-nominal conflict early for a given ten-minute look-ahead window. The CNN based model also showed low levels of false alarm signals as compared to other models. Generally speaking, all models showed low probabilities of miss-detection, mostly in the early phase of the 10-minute look-ahead window. This novel approach may serve to develop effective CD algorithms with longer look-ahead time and may aid in early detection of air traffic conflicts in non-nominal scenarios. |
| first_indexed | 2024-10-01T02:31:59Z |
| format | Conference Paper |
| id | ntu-10356/169380 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:31:59Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1693802023-07-18T15:30:46Z Image-based conflict detection with convolutional neural network under weather uncertainty Dang, Phuoc Huu Mohamed Arif Bin Mohamed Alam, Sameer School of Mechanical and Aerospace Engineering 2023 Integrated Communication, Navigation and Surveillance Conference (ICNS) Air Traffic Management Research Institute Engineering::Aeronautical engineering::Aviation Engineering::Aeronautical engineering::Air navigation Conflict Detection Air Traffic Management Detection of air traffic conflicts in a weather constrained airspace is challenging given the inherent uncertainties and aircraft maneuvers which give rise to new conflict birth-points. Traditional conflict detection tools are untenable in such situations as they primarily rely on flight-plan, aircraft performance characteristics and trajectories projection in short-term (2-4 minutes). This work adopts a convolutional neural network (CNN) model, on radar-like images, for conflict detection task in a constrained airspace. The CNN models are well-known for their learning capabilities when dealing with unstructured data like pixelated images. In this study, historical ADS-B data with weather constrained airspace is input as pixelated images to the CNN model. The learned model was compared with two well-known models for conflict detection (CD). The results demonstrated that the CNN based model was able to predict off-nominal conflict with high accuracy. The CNN model also demonstrated its ability to predict off-nominal conflict early for a given ten-minute look-ahead window. The CNN based model also showed low levels of false alarm signals as compared to other models. Generally speaking, all models showed low probabilities of miss-detection, mostly in the early phase of the 10-minute look-ahead window. This novel approach may serve to develop effective CD algorithms with longer look-ahead time and may aid in early detection of air traffic conflicts in non-nominal scenarios. Civil Aviation Authority of Singapore (CAAS) National Research Foundation (NRF) Submitted/Accepted version This research was supported by the National Research Foundation, Singapore, and the Civil Aviation Authority of Singapore, under the Aviation Transformation Programme. 2023-07-17T06:14:59Z 2023-07-17T06:14:59Z 2023 Conference Paper Dang, P. H., Mohamed Arif Bin Mohamed & Alam, S. (2023). Image-based conflict detection with convolutional neural network under weather uncertainty. 2023 Integrated Communication, Navigation and Surveillance Conference (ICNS). https://dx.doi.org/10.1109/ICNS58246.2023.10124287 979-8-3503-3362-6 2155-4951 https://hdl.handle.net/10356/169380 10.1109/ICNS58246.2023.10124287 en 001332-00004 © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/ICNS58246.2023.10124287. application/pdf |
| spellingShingle | Engineering::Aeronautical engineering::Aviation Engineering::Aeronautical engineering::Air navigation Conflict Detection Air Traffic Management Dang, Phuoc Huu Mohamed Arif Bin Mohamed Alam, Sameer Image-based conflict detection with convolutional neural network under weather uncertainty |
| title | Image-based conflict detection with convolutional neural network under weather uncertainty |
| title_full | Image-based conflict detection with convolutional neural network under weather uncertainty |
| title_fullStr | Image-based conflict detection with convolutional neural network under weather uncertainty |
| title_full_unstemmed | Image-based conflict detection with convolutional neural network under weather uncertainty |
| title_short | Image-based conflict detection with convolutional neural network under weather uncertainty |
| title_sort | image based conflict detection with convolutional neural network under weather uncertainty |
| topic | Engineering::Aeronautical engineering::Aviation Engineering::Aeronautical engineering::Air navigation Conflict Detection Air Traffic Management |
| url | https://hdl.handle.net/10356/169380 |
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