Sun sensor calibration algorithms: A systematic mapping and survey

2025-07-29

AI Summary

Sun sensor calibration is adversely affected by time-varying uncertainties arising from manufacturing tolerances, electrical imperfections, and environmental variations; existing modeling and calibration approaches lack systematic integration. This paper conducts a systematic literature mapping study covering two decades of research, introducing— for the first time—a comprehensive classification framework for calibration algorithms applicable across mainstream sensor configurations. It proposes a unified three-dimensional analytical paradigm integrating error compensation, parameter estimation, and optimization strategies. The study identifies three critical research gaps: dynamic nonlinear modeling, on-orbit self-calibration, and cross-platform generalization. As the first structured methodology review in this domain, the work clarifies fundamental technical bottlenecks and evolutionary pathways, thereby providing both theoretical foundations and practical guidance for high-precision, robust spacecraft attitude determination.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Attitude sensors determine the spacecraft attitude through the sensing of an astronomical object, field or other phenomena. The Sun and fixed stars are the two primary astronomical sensing objects. Attitude sensors are critical components for the survival and knowledge improvement of spacecraft. Of these, sun sensors are the most common and important sensor for spacecraft attitude determination. The sun sensor measures the Sun vector in spacecraft coordinates. The sun sensor calibration process is particularly difficult due to the complex nature of the uncertainties involved. The uncertainties are small, difficult to observe, and vary spatio-temporally over the lifecycle of the sensor. In addition, the sensors are affected by numerous sources of uncertainties, including manufacturing, electrical, environmental, and interference sources. This motivates the development of advanced calibration algorithms to minimize uncertainty over the sensor lifecycle and improve accuracy. Although modeling and calibration techniques for sun sensors have been explored extensively in the literature over the past two decades, there is currently no resource that consolidates and systematically reviews this body of work. The present review proposes a systematic mapping of sun sensor modeling and calibration algorithms across a breadth of sensor configurations. It specifically provides a comprehensive survey of each methodology, along with an analysis of research gaps and recommendations for future directions in sun sensor modeling and calibration techniques.

Problem

• Calibrating sun sensors to minimize uncertainties in spacecraft attitude determination
• Addressing complex spatio-temporal uncertainties in sun sensor measurements
• Systematically reviewing and consolidating sun sensor calibration algorithms

Innovation

• Systematic mapping of sun sensor calibration algorithms
• Comprehensive survey of methodologies and research gaps
• Analysis of future directions in sensor calibration