Blind system identification using the empirical characteristic function's derivative

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High-order statistics have become a common tool in blind identification of nonminimum phase systems. In this paper we present a new, alternative tool, namely the first-order derivatives of the observations' second characteristic function, evaluated at arbitrary (off-origin) locations. The estimation of these derivatives reduces plainly into specially-weighted empirical averages, from which the identification of the system's zeros is nearly straightforward. We show that despite the addition of some nuisance parameters, this approach generates more equations than unknowns, and thus enables a well-averaged least-squares solution. We demonstrate, using simulation results, the potential improvement in estimation accuracy over cumulants-based estimation.

Original languageEnglish
Pages (from-to)II/1733-II/1736
JournalProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
StatePublished - 2002
Event2002 IEEE International Conference on Acoustic, Speech and Signal Processing - Orlando, FL, United States
Duration: 13 May 200217 May 2002


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