Identification of nonlinear systems by correlation using pseudorandom signals

  • Simon N. Obidegwu

Student thesis: Doctoral ThesisDoctor of Philosophy


This thesis is concerned with the measurement of the characteristics of nonlinear systems by crosscorrelation, using pseudorandom input signals based on m sequences. The systems are characterised by Volterra series, and analytical expressions relating the rth order Volterra kernel to r-dimensional crosscorrelation measurements are derived.
It is shown that the two-dimensional crosscorrelation measurements are related to the corresponding second order kernel values by a set of equations which may be structured into a number of independent subsets. The m sequence properties determine how the maximum order of the subsets for off-diagonal values is related to the upper bound of the arguments for nonzero kernel values. The upper bound of the arguments is used as a performance index, and the performance of antisymmetric pseudorandom binary, ternary and quinary signals is investigated.
The performance indices obtained above are small in relation to the periods of the corresponding signals. To achieve higher performance with ternary signals, a method is proposed for combining the estimates of the second order kernel values so that the effects of some of the undesirable nonzero values in the fourth order autocorrelation function of the input signal are removed.
The identification of the dynamics of two-input, single-output systems
with multiplicative nonlinearity is investigated. It is shown that the characteristics of such a system may be determined by crosscorrelation experiments using phase-shifted versions of a common
signal as inputs. The effects of nonlinearities on the estimates of system weighting functions obtained by crosscorrelation are also investigated.
Results obtained by correlation testing of an industrial process are presented, and the differences between theoretical and experimental results discussed for this case;
Date of Award1974
Original languageEnglish
SupervisorH.A. Barker (Supervisor)


  • nonlinear systems
  • correlation
  • pseudorandom signals

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