Open AccessStructured non‐linear noise behaviour and the use of median averaging in non‐linear systems with m ‐sequence inputs
Author(s) -
Wong Hin Kwan,
Schoukens Johan,
Godfrey Keith R.
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2012.0622
Subject(s) - noise (video) , mathematics , parametric statistics , pseudorandom binary sequence , sequence (biology) , gaussian noise , contrast (vision) , identification (biology) , gaussian , linear prediction , algorithm , linear system , binary number , computer science , control theory (sociology) , statistics , artificial intelligence , physics , arithmetic , mathematical analysis , botany , control (management) , quantum mechanics , biology , image (mathematics) , genetics
In non‐linear system identification, results from traditional non‐parametric identification techniques contain both linear and non‐linear contributions. When Gaussian excitation signals (including random‐phased multisines) are used, the non‐linear contributions are noise‐like and therefore not easy to distinguish from environment noise and measurement noise. In contrast, when excitation signals based on binary maximum‐length sequences ( m ‐sequences) are used, a particular property of the sequences results in the non‐linear contributions being structured. It is shown in this study that it is possible to take advantage of this structure by using a median‐based averaging technique, rather than the more traditional arithmetic mean‐based averaging, to obtain better identification performance.