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Joint Satellite-Ground Control Design for Low-Altitude Unmanned Traffic Management via Geostationary Satcom Arrays
Subject area: Science,Engineering and Technology · Area of research: UTM, Satellite Communications, Aerospace Systems
DOI: 10.64388/ire1724810
Abstract
The Research Team (AUAVL/NASRDA & FUTO) addresses the "Bit-Transport Deconfliction Disconnect" inherent in low-altitude Unmanned Traffic Management (UTM) operations below 400 feet AGL. By treating UTM as an integrated Joint Communication, Sensing, and Intelligence (JCSI) optimization problem, the investigators couple multi-agent trajectory optimization loops directly with physical network propagation profiles from the NigComSat-1R geostationary satellite infrastructure. The team deploys a comparative modeling framework leveraging Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to bridge the gap between aerospace control theory and satellite telecommunications.
Keywords
Unmanned traffic management; geostationary satellite; NigComSat-1R; response surface methodology; artificial neural network; ANFIS
References
[1] Anyanwu OA, Nnali-Uroh E, Anyanwu PU. Joint satellite-ground control design for low-altitude unmanned traffic management via geostationary satcom arrays. Journal of Engineering Research. 2026.
[2] NigComSat. NigComSat-1R C-, Ku-, and Ka-band transponder technical specifications. Abuja, Nigeria: NigComSat; 2025.
[3] Jang JSR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern. 1993;23(3):665–685. https://doi.org/10.1109/21.256541
How to cite this paper
@article{1724810,
author = {Anyanwu Obioma Anselam, Patrick Udochukwu Anyanwu; Imaobong Ephraim Essien, Onwuegbuchu Matthew Ekene; Ijeoma Precious Ugwu, Yunusa Abubakar Aliyu; Opara Patricia Uchechi; Okolie Chukwuemeka Anselm, Ogbonna George Onyegbule; Onyebuchi Chinaza Francis; Jude Adekunle Adeleke},
title = {Joint Satellite-Ground Control Design for Low-Altitude Unmanned Traffic Management via Geostationary Satcom Arrays},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {5},
pages = {1270-1273},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724810.pdf},
abstract = {The Research Team (AUAVL/NASRDA & FUTO) addresses the "Bit-Transport Deconfliction Disconnect" inherent in low-altitude Unmanned Traffic Management (UTM) operations below 400 feet AGL. By treating UTM as an integrated Joint Communication, Sensing, and Intelligence (JCSI) optimization problem, the investigators couple multi-agent trajectory optimization loops directly with physical network propagation profiles from the NigComSat-1R geostationary satellite infrastructure. The team deploys a comparative modeling framework leveraging Response Surface Methodology (RSM), Artificial Neural Networks (ANN), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to bridge the gap between aerospace control theory and satellite telecommunications.},
keywords = {Unmanned traffic management; geostationary satellite; NigComSat-1R; response surface methodology; artificial neural network; ANFIS},
month = {November},
doi = {https://doi.org/10.64388/ire1724810}
}