M-estimator-based robust estimation of the number of components of a superimposed sinusoidal signal model |
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Authors: | Sharmishtha Mitra |
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Institution: | 1. Department of Mathematics &2. Statistics, Indian Institute of Technology, Kanpur 208016, India |
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Abstract: | In this paper, we consider the problem of estimating the number of components of a superimposed nonlinear sinusoids model of a signal in the presence of additive noise. We propose and provide a detailed empirical comparison of robust methods for estimation of the number of components. The proposed methods, which are robust modifications of the commonly used information theoretic criteria, are based on various M-estimator approaches and are robust with respect to outliers present in the data and heavy-tailed noise. The proposed methods are compared with the usual non-robust methods through extensive simulations under varied model scenarios. We also present real signal analysis of two speech signals to show the usefulness of the proposed methodology. |
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Keywords: | Akaike information criterion Bayesian information criterion information theoretic criteria M-estimator penalized log-likelihood robust order estimation superimposed sinusoidal signal |
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