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Near Minimax Line Spectral Estimation.

Authors :
Tang, Gongguo
Bhaskar, Badri Narayan
Recht, Benjamin
Source :
IEEE Transactions on Information Theory. Jan2015, Vol. 61 Issue 1, p499-512. 14p.
Publication Year :
2015

Abstract

This paper establishes a nearly optimal algorithm for denoising a mixture of sinusoids from noisy equispaced samples. We derive our algorithm by viewing line spectral estimation as a sparse recovery problem with a continuous, infinite dictionary. We show how to compute the estimator via semidefinite programming and provide guarantees on its mean-squared error rate. We derive a complementary minimax lower bound on this estimation rate, demonstrating that our approach nearly achieves the best possible estimation error. Furthermore, we establish bounds on how well our estimator localizes the frequencies in the signal, showing that the localization error tends to zero as the number of samples grows. We verify our theoretical results in an array of numerical experiments, demonstrating that the semidefinite programming approach outperforms three classical spectral estimation techniques. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
00189448
Volume :
61
Issue :
1
Database :
Academic Search Index
Journal :
IEEE Transactions on Information Theory
Publication Type :
Academic Journal
Accession number :
100150902
Full Text :
https://doi.org/10.1109/TIT.2014.2368122