Volume 38, Number 3, June 2020
|Page(s)||515 - 522|
|Published online||06 August 2020|
A Sliding Window Optimal Tracking Differentiator Filtering Method for Satellite Telemetry Data
Northwestern Polytechnical University, Xi'an 710072, China
2 National Key Laboratory of Aerospace Flight Dynamics, Xi'an 710072, China
The initial satellite telemetry data acquired by ground stations usually contain noise and outlier interference. In order to ensure the accurate analysis of satellite status, the telemetry data need to be filtered. In this paper, a sliding window optimal tracking differentiator filtering (SWOTDF) method for satellite telemetry data is proposed. Aiming at the problem of parameter selection during the filtering of the optimal tracking differentiator, the amplitude-frequency characteristics of the maximum tracking differentiator are analyzed by sine sweep frequency method, and the mapping relationship between tracking factors and filtering effects is established. On this basis, the telemetry data are divided by sliding windows, and the relationship between local stability of data in each window and tracking factors is further analyzed. The calculation method of local data tracking factor is given to realize dynamic optimal tracking differentiator filtering of telemetry data in each window. Experimental results show that the SWOTDF method can effectively avoid the limitations of traditional digital filters in processing nonlinear telemetry data, and can effectively filter out noise and outliers in satellite telemetry data.
Key words: optimal tracking differentiator / sliding window / satellite telemetry data / nonstationary filtering
关键字 : 最速跟踪微分器 / 滑动窗口 / 卫星遥测数据 / 非平稳数据滤波
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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