TY - JOUR
T1 - APPROXIMATE REALIZATION AND MODEL REDUCTION OF NON-MINIMUM PHASE STOCHASTIC SYSTEMS.
AU - Giannakis, Georgios B.
AU - Mendel, Jerry M.
PY - 1986
Y1 - 1986
N2 - The authors deal with the stochastic realization and reduction of non-minimum-phase, linear, time-invariant systems. Assuming that the input is non-Gaussian, stationary and white, they propose the use of second and higher-order output statistics to identify a finite-dimensional ARMA model. Both the input/output, and state space approaches are considered. It is shown that higher-order output cumulants are useful in estimating the AR coefficients of the minimum-phase (MP) part, and in providing a means for model order determination. Higher order cumulants are exploited to realize the all-pass part of the ARMA model.
AB - The authors deal with the stochastic realization and reduction of non-minimum-phase, linear, time-invariant systems. Assuming that the input is non-Gaussian, stationary and white, they propose the use of second and higher-order output statistics to identify a finite-dimensional ARMA model. Both the input/output, and state space approaches are considered. It is shown that higher-order output cumulants are useful in estimating the AR coefficients of the minimum-phase (MP) part, and in providing a means for model order determination. Higher order cumulants are exploited to realize the all-pass part of the ARMA model.
UR - http://www.scopus.com/inward/record.url?scp=0023017074&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0023017074&partnerID=8YFLogxK
U2 - 10.1109/cdc.1986.267545
DO - 10.1109/cdc.1986.267545
M3 - Conference article
AN - SCOPUS:0023017074
SN - 0191-2216
SP - 1079
EP - 1084
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
ER -