Star Tracker Misalignment Compensation in Deep Space Navigation Through Model-Based Estimation
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915410995052544 |
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| author | Ganganath, Ridma Servadio, Simone Lee, David |
| author_facet | Ganganath, Ridma Servadio, Simone Lee, David |
| contents | This work presents a novel adaptive framework for simultaneously estimating spacecraft attitude and sensor misalignment. Uncorrected star tracker misalignment can introduce significant pointing errors that compromise mission objectives in GPS-denied environments. To address this challenge, the proposed architecture integrates a Bayesian Multiple-Model Adaptive Estimation (MMAE) framework operating over an N x N x N 3D hypothesis grid. Each hypothesis employs a 9-state Multiplicative Extended Kalman Filter (MEKF) to estimate attitude, angular velocity, and gyroscope bias using TRIAD-based vector measurements. A key contribution is the development of a robust grid refinement strategy that uses hypothesis diversity and weighted-mean grid centering to prevent the premature convergence commonly encountered in classical, dominant model-based refinement triggers. Extensive Monte Carlo simulations demonstrate that the proposed method reduces the final misalignment RMSE relative to classical approaches, achieving arcsecond-level accuracy. The resulting framework offers a computationally tractable and statistically robust solution for in-flight calibration, enhancing the navigational autonomy of resource-constrained spacecraft. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19838 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Star Tracker Misalignment Compensation in Deep Space Navigation Through Model-Based Estimation Ganganath, Ridma Servadio, Simone Lee, David Systems and Control This work presents a novel adaptive framework for simultaneously estimating spacecraft attitude and sensor misalignment. Uncorrected star tracker misalignment can introduce significant pointing errors that compromise mission objectives in GPS-denied environments. To address this challenge, the proposed architecture integrates a Bayesian Multiple-Model Adaptive Estimation (MMAE) framework operating over an N x N x N 3D hypothesis grid. Each hypothesis employs a 9-state Multiplicative Extended Kalman Filter (MEKF) to estimate attitude, angular velocity, and gyroscope bias using TRIAD-based vector measurements. A key contribution is the development of a robust grid refinement strategy that uses hypothesis diversity and weighted-mean grid centering to prevent the premature convergence commonly encountered in classical, dominant model-based refinement triggers. Extensive Monte Carlo simulations demonstrate that the proposed method reduces the final misalignment RMSE relative to classical approaches, achieving arcsecond-level accuracy. The resulting framework offers a computationally tractable and statistically robust solution for in-flight calibration, enhancing the navigational autonomy of resource-constrained spacecraft. |
| title | Star Tracker Misalignment Compensation in Deep Space Navigation Through Model-Based Estimation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2507.19838 |