Robust Synthetic Air Data Estimation via Kalman-Aided Deep Learning Approach for Analytical Redundancy

Reliable air data is essential for safe and stable flight operations. However, physical air data sensors are susceptible to failure due to environmental disturbances, especially in ultralight manned aircraft where hardware redundancy is often impractical due to strict size, weight, and power constraints. Although model-based synthetic air data systems (SADS) have been proposed to reduce sensor reliance, they require precise aerodynamic coefficients and are sensitive to modeling errors. To address these limitations, this study proposes a lightweight, data-driven SADS framework based on a hybrid deep learning model that combines temporal and trend-based features. An unscented Kalman filter (UKF) is applied as a post-processing step to enhance robustness against noise and anomalous inputs. The system is trained and validated on real-world flight data and demonstrates improved accuracy and stability over conventional deep learning baselines. These results suggest that the proposed method offers a robust and complementary alternative to model-based SADS, particularly in resource-constrained flight environments.