Attribution and Uncertainty Behavior of Learned Residual Gyro Correction for Gyro-Stellar Estimation
This work investigates uncertainty decomposition and explainability in a deep learning-based framework for gyroscope bias correction. A 1-D Convolutional Neural Network is trained to predict residual angular rate corrections from multi-sensor inputs, including gyroscope and star tracker measurements. The bias corrections are sent to a flight-representative Gyro-Stellar Estimator. The network produces both mean corrections and input-dependent (heteroscedastic) aleatoric uncertainty, while epistemic uncertainty is estimated via an ensemble of independently trained models. The proposed approach is trained under nominal conditions and evaluated in both nominal and structured perturbations tha...
| Authors | Mariela De Lucas Álvarez, Melvin Laux, Arthur de Freitas Precht et al. |
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| Primary category | cs.LG |
| Published | 27 July 2026 |
| Last updated at source | 27 July 2026 |
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