Semiblind identification of nonminimum-phase ARMA models via orderrecursion with higher order cumulants
Chow, T.W.S.
Hong-Zhou Tan
Dept. of Electron. Eng., City Univ. of Hong Kong ;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Aug 1998
Volume: 45,
Issue: 4
On page(s): 663-671
ISSN: 0278-0046
References Cited: 18
CODEN: ITIED6
INSPEC Accession Number: 6000795
Digital Object Identifier: 10.1109/41.704896
Current Version Published: 2002-08-06
Abstract
This paper develops a novel identification methodology for
nonminimum-phase autoregressive moving average (ARMA) models of which
the models' orders are not given. It is based on the third-order
statistics of the given noisy output observations and assumed input
random sequences. The semiblind identification approach is thereby
named. By the order-recursive technique, the model orders and parameters
can be determined simultaneously by minimizing well-defined cost
functions. At each updated order, the AR and MA parameters are estimated
without computing the residual time series (RTS), with the result of
decreasing the computational complexity and memory consumption. Effects
of the AR estimation error on the MA parameters estimation are also
reduced. Theoretical statements and simulations results, together with
practical application to the train vibration signals' modeling,
illustrate that the method provides accurate estimates of unknown linear
models, despite the output measurements being corrupted by arbitrary
Gaussian noises of unknown pdf
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