基于双树复小波和形态学的卫星异常数据滤波方法

受系统结构复杂、工作环境恶劣等因素影响,卫星的工作状态会存在介于正常和故障之间的异常状态,导致其遥测数据中不仅含有噪声,还存在异常数据。采用传统滤波算法进行去噪处理时,会存在对异常数据保留不充分的问题,从而导致突变信息丢失。针对上述问题,提出一种基于双树复小波与形态学滤波的卫星遥测数据组合滤波方法,设计一种半软阈值滤波函数来提高双树复小波的滤波性能,并提出一种组合滤波算法。通过仿真实验进行验证,结果表明:本文方法可以对遥测数据中的噪声进行有效滤除并对异常数据进行保留。

The Telemetry Data Filtering Method based on Double Tree Complex Wavelet and Morphology

Influenced by complex structure and poor working conditions, the working status of satellite will have abnormal state that between normal and fault, which would cause not only noise, but also abnormal data existed in the telemetry data. However, when the traditional data filtering algorithm is used to denoising, the abnormal data as well as the related information could be lost. Therefore, a combined filtering method of telemetry data based on double tree complex wavelet and morphological filtering is proposed in this paper. To be specific, a semi-soft threshold filter function is designed to improve the filtering performance. Thus, a combined filtering algorithm is proposed. The experimental results show that the proposed method can effectively filter the noise in the telemetry data and retain the abnormal data.