Satellite Telemetry Parameter Prediction Based on Improved Combinatorial Machine Learning

The monitoring, analysis and anomaly detection of the operational status of satellites in orbit are important contents of satellite operational management. It is very necessary to predict the changing trend of satellite telemetry parameter data series for detecting, dealing with satellite anomalies and ensuring the safe operation of satellites. Aiming at the problem that the current prediction research is not accurate enough for telemetry parameters with insignificant periodicity and multiple changing characteristics, this paper introduces covariates that are helpful for telemetry parameter sequence prediction, and proposes a prediction model based on improved combined machine learning, using the global model and the local model to obtain the trend characteristics and local irregular fluctuation characteristics of the telemetry parameter sequence respectively, and the improved Attention mechanism is used to capture the correlation between multi-dimensional parameters to improve the prediction accuracy. At the same time, this model can provide point prediction and interval prediction results for the telemetry data sequence, providing more input for the decision-making of on-orbit satellite disposal. The validity of the proposed method is verified on the real telemetry data set of scientific satellites and the public time series data sets.