An interpolation method for temperature telemetry data of one-dimensional low-sampling satellite panel based on SE-TCN

doi:10.7523/j.ucas.2022.032

Abstract

Abstract: This paper proposes an autoregressive prediction method based on time convolutional network with attentional mechanism(time convolution network with squeeze and excitation, SE-TCN), to solve the problem of missing telemetry data of satellite panel temperature due to short entry time, framing error, and other reasons. Temperature telemetry data is considered to be a strong regularity of periodic signal, so this paper adopts the SE-TCN model to map from historical data to the data in the future, which completes the missing value interpolation and effectively solves the problem that the interpolation deviation of the physical model and statistical method is too large. At the same time, in order to characterize the interpolation effect on the actual missing data set, the calculation method of the evaluation index is added in this paper. Compared with long short-term memory network and time convolutiion network models, SE-TCN has a better interpolation effect on both the test set and the actual missing data set.

Key words: telemetry data, time series data, missing value interpolation, time convolution network, low sampling

CLC Number:

TP391

XU Kaikai, ZHANG Rui. An interpolation method for temperature telemetry data of one-dimensional low-sampling satellite panel based on SE-TCN[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(6): 810-820.

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