# Sun sensor calibration algorithms: A systematic mapping and survey

arXiv: https://arxiv.org/abs/2507.21541 · HTML: https://mlsanity.com/paper/2507.21541/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 162 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [KalmanNet: Neural Network Aided Kalman Filtering for Partially Known Dynamics](https://mlsanity.com/paper/2107.10043/) (2021)
- [AEGNN: Asynchronous Event-based Graph Neural Networks](https://mlsanity.com/paper/2203.17149/) (2022)
- [Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces](https://mlsanity.com/paper/1905.07357/) (2019)
- [Pose Estimation for Non-Cooperative Rendezvous Using Neural Networks](https://mlsanity.com/paper/1906.09868/) (2019)
- [The Final Frontier: Deep Learning in Space](https://mlsanity.com/paper/2001.10362/) (2020)
- [Latent-KalmanNet: Learned Kalman Filtering for Tracking from High-Dimensional Signals](https://mlsanity.com/paper/2304.07827/) (2023)
- [Adaptive KalmanNet: Data-Driven Kalman Filter with Fast Adaptation](https://mlsanity.com/paper/2309.07016/) (2023)
- [Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks](https://mlsanity.com/paper/2303.08812/) (2023)
- [Real-Time Convolutional Neural Network-Based Star Detection and Centroiding Method for CubeSat Star Tracker](https://mlsanity.com/paper/2404.19108/) (2024)

## Abstract (arXiv)

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.