Design of Quadratic Estimators using Covariance Information in Linear Discrete-Time Stochastic Systems ⋆
نویسندگان
چکیده
This paper, apart from the polynomial estimation technique based on the statespace model, examines to develop an estimation method for the quadratic estimation problem by applying the multivariate RLS Wiener estimator to the quadratic estimation of a stochastic signal in linear discrete-time stochastic systems. The augmented signal vector includes the signal to be estimated and its quadratic quantity. The signal vector is modeled in terms of an AR model of appropriate order. A numerical simulation example for the speech signal as a practical stochastic signal is implemented and its estimation accuracy is fairly improved in comparison with the existing RLS Wiener estimators. It is advantageous that the proposed method can be applied to the quadratic estimations of wide-sense stationary stochastic signals in general.
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