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Regularized Spectral Analysis of Unevenly Spaced Data
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
High resolution spectral analysis has recently been addressed as an inverse problem, and solutions are currently proposed through the regularization framework. In this paper, we focus on regularized spectral analysis of unevenly sampled data for line spectra estimation. First, we study the structural differences of the model between regular sampling, missing data (where the sampling is regular, but with missing data) and irregular sampling cases. Then, consequences for the computation of thedoi:10.1109/icassp.2005.1416035 dblp:conf/icassp/BourguignonCJ05 fatcat:ijaaz5p5izeplfhu4dqsflkzne