Open AccessDe-Noising Algorithm for Flight Data Recording System Based on Modified Ensemble Empirical Mode Decomposition
Author(s) -
Shaowu Dai,
Qiangqiang Chen,
Hongde Dai
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1267/1/012024
Subject(s) - outlier , noise (video) , computer science , hilbert–huang transform , stability (learning theory) , algorithm , mode (computer interface) , data mining , anomaly detection , artificial intelligence , pattern recognition (psychology) , machine learning , image (mathematics) , computer vision , filter (signal processing) , operating system
The Flight Data Recording System (FDRS) records a lot of parameters of the aircraft during flight, which can be used for the test-flying, training mission of aircraft and so on. Effected by the working environment, information interference and its non-stability, the outliers and noise often exists in the FDRS data. These noises and outliers have a great impact on the use of FDRS. The aim of this paper is to remove outliers and de-noising of navigation data in FDRS. The causes of outliers and noise in FDRS data are analyzed firstly, with a reference suggestion proposed. Then the Letts criterion is used to remove outliers and the Modified Ensemble Empirical Mode Decomposition (MEEMD) is applied to achieve denoising for FDRS. Results demonstrate that outliers are removed and the navigation data are de-noised effectively.