Fitting ARMA Models to linear non-Gaussian processes using higher order statistics
Signal Processing - Image and Video Coding beyond Standards
Blind parametric identification of non-Gaussian FIR systems using higher order cumulants
International Journal of Systems Science
Higher-order statistics based blind estimation of non-Gaussian bidimensional moving average models
Signal Processing - Fractional calculus applications in signals and systems
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This paper addresses the problem of estimating the parameters of a moving average (MA) model from either only third- or fourth-order cumulants of the noisy observations of the system output. The system is driven by an independent and identically distributed non-Gaussian sequence that is not observed. The unknown model parameters are obtained using a batch least squares method. Recursive methods are also developed and used to claim the uniqueness of the batch least squares solutions. A novel technique for the enhancement of third-order cumulants of MA processes is introduced. This new technique is based on the concept of composite property mappings and helps reduce the variance of the estimates of third- (or fourth)-order cumulants of MA processes. Simulation results are presented that demonstrate the performance of the new methods and compare them with a range of existing techniques