Ömrünü Tamamlamış Yer Sabit Uyduların Boylam Hareketlerinin Yapay Sinir Ağları ile Modellenmesi

Yıl 2024, Cilt: 36 Sayı: 1, 459 - 470, 28.03.2024

İbrahim Öz , Cevat Özarpa

https://doi.org/10.35234/fumbd.1417170

Öz

Bu çalışmada, yapay sinir ağları kullanılarak işletme ömrünü tamamlamış yer sabit yörünge uydularının boylam hareketleri incelenmiştir. Uydu yörünge hareketleri ve dinamiği içinde, uydu boylam hareketleri yapay sinir ağları ile modellenmiştir. Ömrünü tamamlamış altı uyduya ait veriler, veri tabanından alınmış, kapsamlı bir ön işlemeye tabi tutulmuş ve hem tek girişli hem üç girişli yapay sinir ağı eğitiminde kullanılmıştır. Modelleme sonunda ölçülen ve tahmin edilen sonuçlar arasındaki ortalama kare hata (MSE) 1.55x10-3 ve regresyon değeri 1 civarında olup tüm uydular için oldukça başarılı sonuçlar elde edilmiştir. Böylece yapay sinir ağları ile karmaşık yörünge dinamiğinin modellenebildiği görülmüştür. İşletme ömrünü tamamlamış uyduların boylam hareketlerinin yapay sinir ağları ile etkili bir biçimde modellenebildiği görülmektedir. Uydu operatörleri bu tip uyduların uzun vadeli yörünge hareketlerini önerilen yöntem ile tahmin edebilir ve tahminlerini bu konuda alacakları kararlar için destek bilgisi olarak kullanabilir. İlave olarak bu araştırma ömrünü tamamlamış uyduların hareketlerini hassas bir şekilde göstermekte bu durumda daha iyi görev planlaması yapmaya, kaynak optimizasyonuna ve uzay enkazlarının daha iyi yönetilme stratejilerinin geliştirilmesine imkân tanımaktadır.

Anahtar Kelimeler

Ömrünü tamamlamış uydu, yer sabit yörünge, yapay sinir ağları, boylam değişimi, yörünge dinamiği

Modeling Longitudinal Evolution of Decommissioned Geostationary Satellites using Neural Networks

Öz

This study uses neural networks to explore the intricate longitudinal progression of decommissioned geostationary satellites. The goal is to model and predict satellites' longitudinal dynamics across time dimensions. Historical satellite longitude data undergoes thorough preprocessing to train time series neural networks in both single-input and 3-input configurations for all six decommissioned satellites, yielding comprehensive longitudinal behavior insights. Results reveal impressive outcomes: average Mean Squared Error (MSE) between predicted and measured longitudes is 1.55x10-3, with regression close to unity. This convergence implies a strong alignment between the neural network methodology employed and the intricate problem domain. These results accentuate the suitability and effectiveness of the chosen neural network approach in addressing the challenges posed by decommissioned geostationary satellite trajectory modeling. The study's implications span various fields. Insight into long-term orbital shifts aids in understanding satellite behaviors, enhancing trajectory predictions and decision-making in satellite management and space technology advancement. Additionally the research emphasizes the importance of accurate predictions about satellite behavior after decommissioning. This contributes to better mission planning, resource optimization, and more efficient strategies for dealing with space debris.

Anahtar Kelimeler

Decommissioned satellites, geostationary orbits, neural networks, longitudinal evolution, orbit dynamics.

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