Application of decision tree regression in navigation satellite telemetry data modeling

In order to understand the working state of on orbit satellites, it is necessary to analyze the telemetry data. The fast-changing telemetry data is an important data to express the navigation service status of navigation satellite. Its analysis and modeling are helpful to mine the deep information of navigation telemetry data. A modeling method of on orbit navigation satellite fast-changing telemetry data based on decision tree regression is proposed. The model is used to predict the power measurements at frequency points. The results show that R2 value is greater than 0.96, and the error of prediction value is small. A fast-changing telemetry data model with good effect is established, which provides a possible scheme for the application of artificial intelligence in the analysis of fast-changing telemetry data.