RA
R. Achyuthan
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The detection of non-compliant low-thrust maneuvers in Geosynchronous Earth Orbit (GEO) is hindered by the similarity between thrust accelerations and uncertainties in solar radiation pressure (SRP) modelling. In this thesis, a hybrid architecture is proposed that combines a Long Short-Term Memory (LSTM) classifier with a Physics-Informed Neural Network (PINN) formulated as an inverse-thrust recovery solver. A synthetic dataset of 8400 GEO trajectories across three classes, nominal, low-thrust, and mismodeled SRP, is used for training and evaluation. The LSTM classifier achieved over 88% accuracy on a noisy test set, and thrust vectors were recovered by the PINN with a median magnitude error of 0.9% and directional error of 0.6◦. Superior performance over both a TLE sliding-window method and an Unscented Kalman Filter is demonstrated.
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The detection of non-compliant low-thrust maneuvers in Geosynchronous Earth Orbit (GEO) is hindered by the similarity between thrust accelerations and uncertainties in solar radiation pressure (SRP) modelling. In this thesis, a hybrid architecture is proposed that combines a Long Short-Term Memory (LSTM) classifier with a Physics-Informed Neural Network (PINN) formulated as an inverse-thrust recovery solver. A synthetic dataset of 8400 GEO trajectories across three classes, nominal, low-thrust, and mismodeled SRP, is used for training and evaluation. The LSTM classifier achieved over 88% accuracy on a noisy test set, and thrust vectors were recovered by the PINN with a median magnitude error of 0.9% and directional error of 0.6◦. Superior performance over both a TLE sliding-window method and an Unscented Kalman Filter is demonstrated.