Cyclostationarity: half a century of research
Signal Processing
Bibliography on cyclostationarity
Signal Processing
Blind equalization of a nonlinear satellite system using MCMC simulation methods
EURASIP Journal on Applied Signal Processing
Particle filtering equalization method for a satellite communication channel
EURASIP Journal on Applied Signal Processing
Blind maximum likelihood identification of Hammerstein systems
Automatica (Journal of IFAC)
An adaptive nonlinear filter for system identification
EURASIP Journal on Advances in Signal Processing
Identification of nonlinear additive FIR systems
Automatica (Journal of IFAC)
Quasiconvexity analysis of the Hammerstein model
Automatica (Journal of IFAC)
| Hi-index | 35.69 |
Discrete-time nonlinear models consisting of two linear time invariant (LTI) filters separated by a finite-order zero memory nonlinearity (ZMNL) of the polynomial type (the LTI-ZMNL-LTI model) are appropriate in a large number of practical applications. We discuss some approaches to the problem of blind identification of such nonlinear models, It is shown that for an Nth-order nonlinearity, the (possibly non-minimum phase) finite-memory linear subsystems of LTI-ZMNL and LTI-ZMNL-LTI models can be identified using the N+1th-order (cyclic) statistics of the output sequence alone, provided the input is cyclostationary and satisfies certain conditions. The coefficients of the ZMNL are not needed for identification of the linear subsystems and are not estimated. It is shown that the theory presented leads to analytically simple identification algorithms that possess several noise and interference suppression characteristics