Abstract
Although pseudorandom signals are extremely useful test signals for stimulating a system under identification, there are limits to their usefulness when the system is nonlinear, because of estimate biassing. This paper shows that such biassing depends on the higher-order autocorrelation functions of the pseudorandom signals in a predictable way, and that its effects may be eliminated, or at worst reduced, in most cases. Procedures are proposed for choosing pseudorandom signals from the numerous alternatives available in this application, and for processing the estimates obtained with these signals to minimise the effects of biassing.
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