Blind identification of nonlinear models using higher order spectral analysis
S Prakriya, D Hatzinakos - 1995 International Conference on …, 1995 - ieeexplore.ieee.org
1995 International Conference on Acoustics, Speech, and Signal …, 1995•ieeexplore.ieee.org
A simple method is proposed for blind identification of discrete-time nonlinear models
consisting of two linear time invariant (LTI) subsystems separated by a polynomial-type zero
memory nonlinearity (ZMNL) of order N (the LTI-ZMNL-LTI model). When the input to the
model is a circularly symmetric Gaussian sequence, the linear subsystem of the model can
be identified efficiently using slices of the N+ 1/sup th/order polyspectrum of the output
signal, even when the second linear subsystem is of non-minimum phase (NMP). The ZMNL …
consisting of two linear time invariant (LTI) subsystems separated by a polynomial-type zero
memory nonlinearity (ZMNL) of order N (the LTI-ZMNL-LTI model). When the input to the
model is a circularly symmetric Gaussian sequence, the linear subsystem of the model can
be identified efficiently using slices of the N+ 1/sup th/order polyspectrum of the output
signal, even when the second linear subsystem is of non-minimum phase (NMP). The ZMNL …
A simple method is proposed for blind identification of discrete-time nonlinear models consisting of two linear time invariant (LTI) subsystems separated by a polynomial-type zero memory nonlinearity (ZMNL) of order N (the LTI-ZMNL-LTI model). When the input to the model is a circularly symmetric Gaussian sequence, the linear subsystem of the model can be identified efficiently using slices of the N+1/sup th/ order polyspectrum of the output signal, even when the second linear subsystem is of non-minimum phase (NMP). The ZMNL coefficients need not be known. The order N of the nonlinearity can, in principle, be estimated from the received signal. The methods possess noise suppression characteristics. Computer simulations support the theory.
ieeexplore.ieee.org
以上显示的是最相近的搜索结果。 查看全部搜索结果