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March 21, 2026Sensors0 citationsOpen Access

Simulation of Dense Star Map in Deep Space Based on Gaia Catalogue

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PLPuzhen LiHarbin Institute of TechnologyGBGuangzhen BaoHarbin Institute of TechnologyZZZiwei ZhouHebei Medical University

Key Points

  • The aim is to develop a robust simulation framework for accurate modeling of dense star fields using the Gaia catalog.
  • Integrated simulation framework developed using the Gaia catalog.
  • Coupling of optoelectronic imaging chain with platform disturbances.
  • Incorporation of radiative transfer and noise suites for high fidelity.
  • Achieved Signal-to-Noise Ratio (SNR) error of less than 10%.
  • Sub-pixel centroiding accuracy exceeding 0.01 pixels.
  • Validation against empirical data shows high consistency with real-world observations.

Abstract

High-fidelity star field simulation is paramount for target detection and space situational awareness (SSA) in geostationary and deep-space environments. However, accurately modeling the synergistic effects of ultra-dense stellar backgrounds and complex platform perturbations remains a formidable challenge. This paper proposes an integrated simulation framework that leverages the Gaia catalog to generate high-precision stellar environments. The core methodological novelty lies in the end-to-end coupling of a full optoelectronic imaging chain with dynamic platform disturbances, effectively bridging the gap between theoretical orbital dynamics and realistic sensor responses. Distinguishing itself from conventional models, our approach uniquely integrates radiative transfer and high-fidelity noise suites—including photon shot noise and non-uniform stray light—while utilizing the Gaia catalog to achieve unprecedented precision in simulating dim stars at low magnitudes. The fidelity of the proposed model was quantitatively validated against empirical data from a ground-based wide-field telescope (GTC). Experimental results, derived from multiple simulation realizations, demonstrate high consistency with real-world observations, achieving a Signal-to-Noise Ratio (SNR) error of less than 10% and a sub-pixel centroiding accuracy exceeding 0.01 pixels. This work provides a robust, high-fidelity data synthesis tool that significantly advances the development of target detection algorithms and the performance optimization of space-based optical sensors.

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Cite This Study

Li et al. (2026) studied this question.

synapsesocial.com/papers/69be38ca6e48c4981c679783https://doi.org/10.3390/s26061945
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