GPS alignment from multiple sources to extract aircraft bearing in aerial surveys

• Published in: 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)
• Main Authors: Power, Joshua, Jacoby, Derek, Drouin, Marc-Antoine, Durand, Guillaume, Coady, Yvonne, Meng, Julian
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2023
• Subjects: GPS alignment; multi-source GPS fusion; aerial survey; marine megafauna; machine learning; glare; data-driven mission planning; context-aware; image quality metric; North Atlantic
• Online Access: https://doi.org/10.1109/PLANS53410.2023.10139941; https://nrc-publications.canada.ca/eng/view/object/?id=4f8acc87-308e-4334-8fe2-7c1ea6560353; https://nrc-publications.canada.ca/fra/voir/objet/?id=4f8acc87-308e-4334-8fe2-7c1ea6560353

Methodical aerial population surveys monitoring critically endangered species in Canadian North Atlantic waters are instrumental in influencing government policies both in eco-nomic and conservational efforts. The primary factor hindering the success of these missions is poor visibility caused by glare. This paper builds off our foundational paper [1] and pushes the envelope toward a data-driven glare modelling system. Said data-driven system makes use of meteorological and astronomical data to assist aircraft in navigating in order to mitigate acquisition errors and optimize the quality of acquired data. It is found that reliably extracting aircraft orientation is critical to our approach, to that end, we present a GPS alignment methodology which makes use of the fusion of two GPS signals. Using the complementary strengths and weaknesses of these two signals a synthetic interpolation of fused data is used to generate more reliable flight tracks, substantially improving glare modelling. This methodology could be applied to any other applications with similar signal restrictions. Peer reviewed: Yes NRC publication: Yes