کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
696613 | 890342 | 2013 | 9 صفحه PDF | دانلود رایگان |
This paper presents a second-order statistics based method for blind identification of non-minimum phase single-input–single-output (SISO) auto-regression moving-average (ARMA) systems. By holding the system input while sampling the system output at the normal rate, the SISO system is transformed into an equivalent single-input–multi-output (SIMO) ARMA model. Theoretical analysis is conducted to exploit the system auto-regressive information contained in the autocorrelation matrices of the over-sampled output and to derive expressions for constructive estimation of the ARMA system parameters. The developed systematic identification method has flexibility in choosing the over-sampling rate which can be as low as two. The effectiveness of the proposed method is demonstrated by simulation results.
Journal: Automatica - Volume 49, Issue 6, June 2013, Pages 1846–1854