PREDICTING SATELLITE TELEMETRY HEALTH PARAMETERS USING MACHINE LEARNING

Abstract

In this paper, a methodology is used for predicting time series data which can be used for predicting spacecraft health parameters during non-visible period using the last received health parameters from the spacecraft (simulated parameters). Machine Learning based methodology to monitor telemetry parameters for early detection of anomalies and predicting trends is used. Explored different models and comparing them to see which one gives the best accuracy. We have proposed Machine learning using supervised learning algorithms like Long Short-Term Memory (LSTM) approach as it is well suited for solving this type of regression problem. LSTM is a special case of Recurrent Neural Networks (RNN) which is capable of learning long-term dependencies.