Blind identification of nonlinear models using higher order spectral analysis

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^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