Structured non-linear noise behaviour and the use of median averaging in non-linear systems with m-sequence inputs

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.