Learning-Driven Dual-Line Laser Scanning for Fast and Accurate LEO Satellite Positioning
Mohammad Taghi Dabiri, Rula Ammuri, Mazen Hasna, Khalid Qaraqe

TL;DR
This paper introduces a learning-driven dual-line laser scanning method for fast, accurate, and low-latency LEO satellite positioning, outperforming traditional Gaussian-beam systems in speed and reliability.
Contribution
It proposes a novel dual-line laser scanning framework combined with a physics-based model and neural estimator for precise satellite positioning without mechanical parts.
Findings
Achieves 7-10 m positioning errors with 1-2 ms scanning time.
Outperforms conventional Gaussian-beam schemes in speed and consistency.
Offers a practical solution for next-generation LEO satellite tracking.
Abstract
Accurate and low-latency positioning is a key enabler for optical links with Low Earth Orbit (LEO) satellites, where millisecond-level beam alignment is required to maintain reliable high-data-rate communication. This paper presents a learning-driven dual-line laser scanning framework for fast and precise satellite positioning. Unlike conventional Gaussian-beam acquisition systems that rely on multiple sequential beams or mechanical steering, the proposed approach employs two orthogonal line-shaped laser beams to perform structured optical scanning over the ambiguity region without any moving parts. A physics-based model incorporating atmospheric attenuation, turbulence, and MRR-based reflection is developed, and a data-driven neural estimator is trained to map received optical energy patterns to the satellite's two-dimensional position. Simulation results demonstrate that the…
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Taxonomy
TopicsOptical Wireless Communication Technologies · Space Satellite Systems and Control · Adaptive optics and wavefront sensing
