Abstract
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 language | English |
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Pages (from-to) | 1704-1718 |
Number of pages | 15 |
Journal | IEEE Transactions on Signal Processing |
Volume | 44 |
Issue number | 7 |
DOIs | |
Publication status | Published - Jul 1996 |