Chaos based semi-blind system identification using an EM-UKS estimator

In this paper, we address the problem of parameter estimation of systems driven by chaotic signal We propose an expectation maximization (EM) based unscented Kalman smoother (UKS) to simultaneously estimate parameters of system along with the equalized chaotic signal. The proposed method can be applied to both linear and nonlinear systems driven by chaotic signals. The performance of the proposed estimator is evaluated for identification of systems that occur frequently in communication systems. The estimation performance of the proposed algorithm is evaluated using computer simulations and shown to be better than conventional nonlinear system identification algorithms.