Evaluating Energy and Visibility Trade-Offs in LEO Satellite Edge Computing for Airplane Detection
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| Publikašuvnnas: | ProQuest Dissertations and Theses (2025) |
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ProQuest Dissertations & Theses
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| Liŋkkat: | Citation/Abstract Full Text - PDF |
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| Abstrákta: | This thesis investigates the efficiency of various Low Earth Orbit (LEO) configurations for autonomous airplane detection using onboard edge computing in space. The study evaluates the trade-off between maximizing target visibility—defined as daytime passes suitable for optical detection over Incheon International Airport—and minimizing energy consumption for satellite operations. A baseline Sun-Synchronous Orbit (SSO) is compared against multiple Non-Sun-Synchronous Orbits (NSSOs) with varying inclinations (45°–110°), RAANs, and arguments of perigee. For each configuration, orbital visibility is simulated over 1-day and 7-day windows using Python-based tools with J2 perturbation modeling validated against GMAT. Daytime visibility is filtered using local KST-based illumination constraints. To quantify operational efficiency, a composite scoring system is introduced that aggregates normalized visibility metrics, followed by an energy modeling framework that estimates consumption per image strip and per orbit. The model incorporates realistic assumptions: an optical payload based on KOMPSAT-3 specifications, onboard inference with YOLOv5n on NVIDIA Jetson Xavier NX, and S-band downlink of detection results. A simplified solar generation model evaluates the power budget. The findings reveal that mid-inclination NSSOs (especially at 70°) strike the most effective balance between data yield and energy sustainability. The methodology provides a scalable framework for orbit design tailored to edge-AI missions requiring both high revisit rates and energy efficiency. |
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| ISBN: | 9798293847969 |
| Gáldu: | ProQuest Dissertations & Theses Global |