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Multi-Target Detection With an Arbitrary Spacing Distribution | IEEE Journals & Magazine | IEEE Xplore

Multi-Target Detection With an Arbitrary Spacing Distribution


Abstract:

Motivated by the structure reconstruction problem in single-particle cryo-electron microscopy, we consider the multi-target detection model, where multiple copies of a ta...Show More

Abstract:

Motivated by the structure reconstruction problem in single-particle cryo-electron microscopy, we consider the multi-target detection model, where multiple copies of a target signal occur at unknown locations in a long measurement, further corrupted by additive Gaussian noise. At low noise levels, one can easily detect the signal occurrences and estimate the signal by averaging. However, in the presence of high noise, which is the focus of this paper, detection is impossible. Here, we propose two approaches-autocorrelation analysis and an approximate expectation maximization algorithm-to reconstruct the signal without the need to detect signal occurrences in the measurement. In particular, our methods apply to an arbitrary spacing distribution of signal occurrences. We demonstrate reconstructions with synthetic data and empirically show that the sample complexity of both methods scales as SNR-3 in the low SNR regime.
Published in: IEEE Transactions on Signal Processing ( Volume: 68)
Page(s): 1589 - 1601
Date of Publication: 24 February 2020

ISSN Information:

PubMed ID: 33746466

Funding Agency:


References

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