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Near Minimax Line Spectral Estimation | IEEE Journals & Magazine | IEEE Xplore

Near Minimax Line Spectral Estimation

Publisher: IEEE

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 est...View more

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.
Published in: IEEE Transactions on Information Theory ( Volume: 61, Issue: 1, January 2015)
Page(s): 499 - 512
Date of Publication: 06 November 2014

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Publisher: IEEE

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