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Blind channel identification using higher-order statistics

This article addresses the problem of blind identification of a non-minimum phase system from only third- and fourth-order cumulants of the output noisy observations of the system. Nonlinear optimization algorithms, namely the gradient descent, the Gauss–Newton and the Newton–Raphson algorithms, are proposed for estimating the parameters of the moving average models. A relationship between third- and fourth-order cumulants of the noisy system output and the parameters of the model is exploited to build a set of non-linear equations that is solved by means of the three non-linear optimization algorithms cited above. Simulation results demonstrate the performance of the proposed algorithms.