A Sliding Window Optimal Tracking Differentiator Filtering Method for Satellite Telemetry Data

基于滑动最速跟踪微分器的遥测数据滤波方法

¹ Northwestern Polytechnical University, Xi'an 710072, China
² National Key Laboratory of Aerospace Flight Dynamics, Xi'an 710072, China

Received: 20 March 2019

Abstract

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.

摘要

原始卫星遥测数据中通常含有噪声和野值，为确保对卫星状态的准确分析，需要对遥测数据进行滤波处理。提出一种面向卫星遥测数据的滑动最速跟踪微分器滤波方法，针对最速跟踪微分器的参数选择问题，采用扫频法分析其幅频特性，建立了跟踪因子与滤波结果之间的函数关系，在此基础上，利用滑动窗口划分遥测数据，并分析各窗口内数据局部稳定性与跟踪因子之间的关系，给出了局部数据跟踪因子的计算方法，实现对各窗口内遥测数据的动态滤波。实验结果表明，该方法能够避免传统数字滤波器处理遥测数据的局限性，可以有效滤除卫星遥测数据中的噪声和野值。