This work presents an algorithm to perform autonomous navigation in spacecraft using onboard magnetometer data during GPS outages. An Extended Kalman Filter (EKF) exploiting magnetic field measurements is combined with a Single-Hidden-Layer Feedforward Neural Network (SLFN) trained via the Extreme Learning Machine to improve the accuracy of the state estimate. The SLFN is trained using GPS data when available and predicts the state correction to be applied to the EKF estimates. The CHAOS-7 magnetic field model is used to generate the magnetometer measurements, while a 13th-order IGRF model is exploited by the EKF. Tests on simulated data showed that the algorithm improved the state estimate provided by the EKF by a factor of 2.4 for a total of 51 days when trained on 5 days of GPS data.
The proliferation of reusable space vehicles has fundamentally changed how assets are injected into the low earth orbit and beyond, increasing both the reliability and frequency of launches. Consequently, it has led to the rapid development and adoption of new technologies in the…
A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is dec…
As global airspace operations grow increasingly complex, the risk of near-mid-air collisions (NMACs) poses a persistent and critical challenge to aviation safety. Traditional collision-avoidance systems, while effective in many scenarios, are limited by rule-based logic and relia…
Aerostructure surface damage inspection is carried out over the whole life-cycle using legacy processes and recording during maintenance. The inspection techniques record the detailed history of the damage and repair. However it remains elusive to predict the location of future d…
Understanding rock hardness on extraterrestrial planets offers valuable insights into planetary geological evolution. Rock hardness correlates with morphological parameters, which can be extracted from navigation images, bypassing the time and cost of rock sampling and return. Th…
The use of machine learning techniques to identify contributing factors in air incidents has grown significantly, helping to identify and prevent accidents and improve air safety. In this paper, classifier models such as LS, KNN, Random Forest, Extra Trees, and XGBoost, which hav…