Nonlinear Blind Identification with Three-Dimensional Tensor Analysis

This paper deals with the analysis of a third-order tensor composed of a fourth-order output cumulants used for blind identification of a second-order Volterra-Hammerstein series. It is demonstrated that this nonlinear identification problem can be converted in amultivariable system with multiequations having the form of Ax By c. The system may be solved using several methods. Simulation results with the Iterative Alternating Least Squares IALS algorithm provide good performances for different signal-to-noise ratio SNR levels. Convergence issues using the reversibility analysis of matrices A and B are addressed. Comparison results with other existing algorithms are carried out to show the efficiency of the proposed algorithm.