Different LSTM-Based Approaches to Enhance Satellite Telemetry Compression Prediction

prediction-based methods are utilized to compress satellite telemetry data. In this paper, two-stage lossless compression methods for telemetry data are demonstrated. In the first stage, different approaches of Long Short Term Memory (LSTM) based on a one-to-one, many-to-one, and many-to-many network architectures, are presented. The framework of implementing each approach, as a predictor, is discussed. In the second stage, a set of competing entropy coding methods are tested and evaluated.