MA parameter estimation and cumulant enhancement

A G Stogioglou, Steve McLaughlin

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1704-1718
Number of pages15
JournalIEEE Transactions on Signal Processing
Issue number7
Publication statusPublished - Jul 1996


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