On the robust estimation of the autocorrelation coefficients of stationary sequences

This paper discusses methods for the estimation of the autocorrelation coefficients of a finite-dependent stationary random sequence. Three estimators are examined: the sample average and two proposed approaches, namely the pseudo-maximum-likelihood (pseudo-ML) estimator and the pseudo-M estimator....

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Bibliographic Details
Published in:IEEE transactions on signal processing 1996-10, Vol.44 (10), p.2508-2520
Main Authors: Batalama, S.N., Kazakos, D.
Format: Article
Language:English
Subjects:
Applied sciences
Autocorrelation
Degradation
Detection, estimation, filtering, equalization, prediction
Distribution functions
Exact sciences and technology
Information, signal and communications theory
Integral equations
Maximum likelihood estimation
Minimax techniques
Random sequences
Random variables
Robustness
Signal and communications theory
Signal, noise
Telecommunications and information theory
Testing
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Summary:This paper discusses methods for the estimation of the autocorrelation coefficients of a finite-dependent stationary random sequence. Three estimators are examined: the sample average and two proposed approaches, namely the pseudo-maximum-likelihood (pseudo-ML) estimator and the pseudo-M estimator. The latter scheme is found as a solution of a Fredholm integral equation. All three estimators are first studied for specific distribution models. Then the existence of a minimax robust design is proved and a suboptimally robust scheme is proposed. Simulation results illustrate the theoretical foundations of the methods and indicate that the pseudo-M estimator achieves significantly better performance than the other two schemes when tested against dependent data and in the presence of outliers. Finally, the results may also be applied to the estimation of a location parameter of a dependent random sequence.
ISSN:1053-587X
1941-0476
DOI:10.1109/78.539035