Journal ArticleDOI
Spectral Analysis of Signals:The Missing Data Case
Yanwei Wang,Jian Li,Petre Stoica +2 more
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TLDR
In this article, the authors presented a comprehensive toolset for the missing data problem based exclusively on the nonparametric adaptive filter-bank approaches, which can provide high resolution and low sidelobes.Abstract:
Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and ac urate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.read more
Citations
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On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data
TL;DR: This paper presents a gridless version of SPICE (gridless SPICE, or GLS), which is applicable to both complete and incomplete data without the knowledge of noise level, and proves the equivalence between GLS and atomic norm-based techniques under different assumptions of noise.
Posted Content
On Gridless Sparse Methods for Line Spectral Estimation From Complete and Incomplete Data
TL;DR: In this paper, a gridless version of SPICE (gridless SPICE, or GLS) is presented, which is applicable to both complete and incomplete data without the knowledge of noise level.
Proceedings ArticleDOI
Missing data recovery via a nonparametric iterative adaptive approach
TL;DR: It is shown that MIAA can outperform an existing competitive approach, and this at a much lower computational cost.
Journal ArticleDOI
Efficient Implementation of Iterative Adaptive Approach Spectral Estimation Techniques
TL;DR: This paper presents computationally efficient implementations for several recent algorithms based on the iterative adaptive approach for uniformly sampled one- and two-dimensional data sets, considering both the complete data case and the cases when the data sets are missing samples, either lacking arbitrary locations, or having gaps or periodically reoccurring gaps.
Compressed Sensing o the Grid
TL;DR: In this paper, the frequency components of a mixture of s complex sinusoids from a random subset of n regularly spaced samples are estimated using an atomic norm minimization approach to exactly recover the unobserved samples.
References
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Maximum likelihood from incomplete data via the EM algorithm
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TL;DR: Das Buch behandelt die Systemidentifizierung in dem theoretischen Bereich, der direkte Auswirkungen auf Verstaendnis and praktische Anwendung der verschiedenen Verfahren zur IdentifIZierung hat.
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Studies in astronomical time series analysis. II - Statistical aspects of spectral analysis of unevenly spaced data
TL;DR: This paper studies the reliability and efficiency of detection with the most commonly used technique, the periodogram, in the case where the observation times are unevenly spaced to retain the simple statistical behavior of the evenly spaced case.
Journal ArticleDOI
High-resolution frequency-wavenumber spectrum analysis
TL;DR: In this article, a high-resolution frequency-wavenumber power spectral density estimation method was proposed, which employs a wavenumber window whose shape changes and is a function of the wave height at which an estimate is obtained.