TY - JOUR
T1 - System identification using nonstationary signals
AU - Shalvi, Ofir
AU - Weinstein, Ehud
N1 - Funding Information:
Manuscript received May 6, 1994; revised October 20, 1995. This work was supported in part by the Wolfson Research Award administrated by the Israel Academy of Science and Humanities at Tel-Aviv University, in part by the Wolfson Foundation, in part by the Fulbright Foundation, and in part by the Charles Clore Foundation. The associate editor coordinating the review of this paper and approving it for publication was Prof. Daniel Fuhrman.
PY - 1996
Y1 - 1996
N2 - The conventional method for identifying the transfer function of an unknown linear system consists of a least squares fit of its input to its output. It is equivalent to identifying the frequency response of the system by calculating the empirical cross-spectrum between the system's input and output, divided by the empirical auto-spectrum of the input process. However, if the additive noise at the system's output is correlated with the input process, e.g., in case of environmental noise that affects both system's input and output, the method may suffer from a severe bias effect. In this paper we present a modification of the cross-spectral method that exploits nonstationary features in the data in order to circumvent bias effects caused by correlated stationary noise. The proposed method is particularly attractive to problems of multichannel signal enhancement and noise cancellation, when the desired signal is nonstationary in nature, e.g., a speech or an image.
AB - The conventional method for identifying the transfer function of an unknown linear system consists of a least squares fit of its input to its output. It is equivalent to identifying the frequency response of the system by calculating the empirical cross-spectrum between the system's input and output, divided by the empirical auto-spectrum of the input process. However, if the additive noise at the system's output is correlated with the input process, e.g., in case of environmental noise that affects both system's input and output, the method may suffer from a severe bias effect. In this paper we present a modification of the cross-spectral method that exploits nonstationary features in the data in order to circumvent bias effects caused by correlated stationary noise. The proposed method is particularly attractive to problems of multichannel signal enhancement and noise cancellation, when the desired signal is nonstationary in nature, e.g., a speech or an image.
UR - http://www.scopus.com/inward/record.url?scp=0030214839&partnerID=8YFLogxK
U2 - 10.1109/78.533725
DO - 10.1109/78.533725
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AN - SCOPUS:0030214839
SN - 1053-587X
VL - 44
SP - 2055
EP - 2063
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 8
ER -