Nonparametric spectral analysis of wideband spectrum with missing data via sample-and-hold interpolation and deconvolution

Spectral estimation of data sequences with randomly missing samples is considered in this paper. A nonparametric missing-data method is proposed based on interpolation followed by a deconvolution procedure. Sample-and-hold interpolation is considered here. The method is based on the analytic express...

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Bibliographic Details
Published in:Digital signal processing 2012-12, Vol.22 (6), p.994-1004
Main Authors: Plantier, G., Moreau, S., Simon, L., Valière, J.-C., Le Duff, A., Bailliet, H.
Format: Article
Language:English
Subjects:
Algorithms
Autocorrelation functions
Computer simulation
Deconvolution
Density
Engineering Sciences
Interpolation
Missing data
Monte Carlo methods
Power spectral density estimation
Spectra
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Summary:Spectral estimation of data sequences with randomly missing samples is considered in this paper. A nonparametric missing-data method is proposed based on interpolation followed by a deconvolution procedure. Sample-and-hold interpolation is considered here. The method is based on the analytic expression of the autocorrelation function of the interpolated data as a linear function of the autocorrelation function of the data to be estimated. Bias and standard deviation of both autocorrelation function and power spectral density are detailed for simulated data based on Monte Carlo analysis. The method is also compared with a fuzzy slotting technique with local normalization and weighting algorithm. Based on the results of these simulations, it is concluded that the performance of the proposed method is better than those of the slotting technique.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2012.05.012