A High-Fidelity Star Map Simulation Method for Airborne All-Time Three-FOV Star Sensor Under Dynamic Conditions

To address the lack of reliable test data for evaluating star sensor performance in dynamic airborne environments, this paper presents a high-fidelity star map simulation method for all-time three-Field of View (FOV) star sensors. A comprehensive simulation framework integrating stellar radiation, atmospheric transmission, and detector noise models was developed to accurately model star trailing effects under dynamic conditions. First, a stellar position calculation model incorporating atmospheric refraction correction and platform motion parameters was established through coordinate transformations between the Geocentric Celestial Reference System (GCRS) and FOV coordinate system. Next, a complete energy transfer chain was constructed by combining star catalog data, atmospheric radiative properties, and detector noise characteristics. Finally, a quantitative evaluation system was introduced, employing metrics such as signal-to-noise ratio (SNR), total grayscale value (Gtotal), grayscale concentration index (GCI), and dynamic star displacement (DSD). Field experiments at 2388 m altitude (100.23°E, 26.86°N) demonstrated the average relative error of all evaluation metrics below 9% for static conditions and approximately 8% for dynamic scenarios between simulated and real star maps. The method effectively reproduces stellar radiation, atmospheric noise, and dynamic degradation, providing reliable simulation conditions for airborne star sensor testing and star trailing restoration algorithm development.

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Publication Details

Journal
Remote Sensing
Published
2025-11-28
DOI
https://doi.org/10.3390/rs17233853
Primary Topic
Inertial Sensor and Navigation
Type
article
Field-Weighted Citation Impact
0.00

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article

A High-Fidelity Star Map Simulation Method for Airborne All-Time Three-FOV Star Sensor Under Dynamic Conditions

Jianting Zhou, Xiaodong Gao, Liang Fang, Rujin Zhao et al.
Remote Sensing
Inertial Sensor and Navigation
article

A High-Fidelity Star Map Simulation Method for Airborne All-Time Three-FOV Star Sensor Under Dynamic Conditions

Jianting Zhou, Xiaodong Gao, Liang Fang, Rujin Zhao, Wei Sun, Kaili Lu, Hui Zhang
article en

Abstract

To address the lack of reliable test data for evaluating star sensor performance in dynamic airborne environments, this paper presents a high-fidelity star map simulation method for all-time three-Field of View (FOV) star sensors. A comprehensive simulation framework integrating stellar radiation, atmospheric transmission, and detector noise models was developed to accurately model star trailing effects under dynamic conditions. First, a stellar position calculation model incorporating atmospheric refraction correction and platform motion parameters was established through coordinate transformations between the Geocentric Celestial Reference System (GCRS) and FOV coordinate system. Next, a complete energy transfer chain was constructed by combining star catalog data, atmospheric radiative properties, and detector noise characteristics. Finally, a quantitative evaluation system was introduced, employing metrics such as signal-to-noise ratio (SNR), total grayscale value (Gtotal), grayscale concentration index (GCI), and dynamic star displacement (DSD). Field experiments at 2388 m altitude (100.23°E, 26.86°N) demonstrated the average relative error of all evaluation metrics below 9% for static conditions and approximately 8% for dynamic scenarios between simulated and real star maps. The method effectively reproduces stellar radiation, atmospheric noise, and dynamic degradation, providing reliable simulation conditions for airborne star sensor testing and star trailing restoration algorithm development.

Remote SensingVol. 17(23)
Chinese Academy of Sciences (CN), Institute of Optics and Electronics, Chinese Academy of Sciences (CN), University of Chinese Academy of Sciences (CN)
Chinese Academy of Sciences, National Key Research and Development Program of China
Openalex Percentile: Top 52%
Inertial Sensor and Navigation
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