Compensating Star-Trackers Misalignments with Adaptive Multi-Model Estimation
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arXiv
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| Format: | Preprint |
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2026
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| author | Ganganath, Ridma Servadio, Simone Lee, David Daeyoung |
| author_facet | Ganganath, Ridma Servadio, Simone Lee, David Daeyoung |
| contents | This paper presents an adaptive multi-model framework for jointly estimating spacecraft attitude and star-tracker misalignments in GPS-denied deep-space CubeSat missions. A Multiplicative Extended Kalman Filter (MEKF) estimates attitude, angular velocity, and gyro bias, while a Bayesian Multiple-Model Adaptive Estimation (MMAE) layer operates on a discrete grid of body-to-sensor misalignment hypotheses. In the single-misalignment case, the MEKF processes gyroscope measurements and TRIAD-based attitude observations, and the MMAE updates a three-dimensional grid over the misalignment vector. For a dual-misalignment configuration, the same MEKF dynamics are retained, and the MMAE bank is driven directly by stacked line-of-sight measurements from two star trackers, forming a six-dimensional grid over the two misalignment quaternions without augmenting the continuous-state dimension. A novel diversity metric, $Ψ$, is introduced to trigger adaptive refinement of the misalignment grid around a weighted-mean estimate, thereby preventing premature collapse of the model probabilities and concentrating computation in the most likely region of the parameter space. Monte Carlo simulations show arcsecond-level misalignment estimation and sub-degree attitude errors for both estimation problems, with estimation errors remaining well-bounded, proving robustness and consistency. These results indicate that the proposed MEKF--MMAE architecture enables accurate, autonomous, and computationally efficient in-flight calibration for resource-constrained spacecraft, and establishes dual star-tracker misalignment estimation as a practical option for deep-space CubeSat missions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01130 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Compensating Star-Trackers Misalignments with Adaptive Multi-Model Estimation Ganganath, Ridma Servadio, Simone Lee, David Daeyoung Systems and Control Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics This paper presents an adaptive multi-model framework for jointly estimating spacecraft attitude and star-tracker misalignments in GPS-denied deep-space CubeSat missions. A Multiplicative Extended Kalman Filter (MEKF) estimates attitude, angular velocity, and gyro bias, while a Bayesian Multiple-Model Adaptive Estimation (MMAE) layer operates on a discrete grid of body-to-sensor misalignment hypotheses. In the single-misalignment case, the MEKF processes gyroscope measurements and TRIAD-based attitude observations, and the MMAE updates a three-dimensional grid over the misalignment vector. For a dual-misalignment configuration, the same MEKF dynamics are retained, and the MMAE bank is driven directly by stacked line-of-sight measurements from two star trackers, forming a six-dimensional grid over the two misalignment quaternions without augmenting the continuous-state dimension. A novel diversity metric, $Ψ$, is introduced to trigger adaptive refinement of the misalignment grid around a weighted-mean estimate, thereby preventing premature collapse of the model probabilities and concentrating computation in the most likely region of the parameter space. Monte Carlo simulations show arcsecond-level misalignment estimation and sub-degree attitude errors for both estimation problems, with estimation errors remaining well-bounded, proving robustness and consistency. These results indicate that the proposed MEKF--MMAE architecture enables accurate, autonomous, and computationally efficient in-flight calibration for resource-constrained spacecraft, and establishes dual star-tracker misalignment estimation as a practical option for deep-space CubeSat missions. |
| title | Compensating Star-Trackers Misalignments with Adaptive Multi-Model Estimation |
| topic | Systems and Control Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2601.01130 |