Reinforcement Learning-Driven Framework for High-Precision Target Tracking in Radio Astronomy

Radio astronomy requires precise target localization and tracking to ensure accurate observations. Conventional regulation methodologies, encompassing PID controllers, frequently encounter difficulties due to orientation inaccuracies precipitated by mechanical limitations, environmental fluctuations...

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Главные авторы:	Tanawit Sahavisit, Popphon Laon, Supavee Pourbunthidkul, Pattharin Wichittrakarn, Pattarapong Phasukkit, Nongluck Houngkamhang
Формат:	Artigo
Язык:	Inglês
Опубликовано:	MDPI AG 2025-10-01
Серии:	Galaxies
Предметы:	reinforcement learning radio astronomy surveys time domain target tracking radio telescope control
Online-ссылка:	https://www.mdpi.com/2075-4434/13/6/124
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