Feedback Neural Network Based Orbital Trajectory Prediction

• Autor(a) principal: Senra, Filipe Miguel Santa Maria
• Data de Publicação: 2023
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/10400.6/13958
• topic: Deep Learning; Filtro de Kalman; Filtro de Kalman Extendido; Long Shortterm Memory; Machine Learning; Mecânica Orbital; Previsão de Órbita; Propagação de Órbita; Rede Neuronal; Satélite Leo

In recent years, the number of satellites and debris in space has dangerously increased. For this reason, it is indispensable that tracking and orbit prediction of these objects is performed with the highest level of accuracy. Currently, orbit prediction depends on mathematical models that describe the physics behind the movement of a certain object in space. However, at times, these models can limit the accuracy of the orbit prediction for being characterized by a high degree of complexity and non­linearity. On another note, the application of Machine Learning to the space sector has been increasing rapidly, being of interest to investigate its applicability in the field of orbit prediction. In the present dissertation, a Long Short­Term Memory (LSTM) neural network is designed and investigated. The obtained results are subsequently compared with the results obtained from an Extended Kalman Filter (EKF). With data from a two­line element (TLE) file belonging to the satellite STARLINK­1028, its orbit was propagated for 48h, producing 17281 state vectors that are utilized for training the neural network. A second data set was generated, where Gaussian noise with a distribution N(0, 100) was added. The purpose of this noisy data set is to represent the presence of errors caused by measurements and assess the robustness of the models. A neural network was developed using the Python language and the Tensorflow and Keras libraries, following a Multiple Inputs Single Output (MISO) approach. To test if the performance of the neural network increases the more data is available for training, three case studies were developed, where case studies A, B and C use 41.7%, 83.3% and 100% of the data set, respectively. The models were validated using a pragmatic validation and the more common validation, where it is shown that there are no signs of overfitting or underfitting. Results demonstrate that the models are robust when faced with noisy data and their performance increases with the size of the training set. However, despite the the neural network having been validated and exhibits low prediction errors, the Kalman filter achieved a better performance.