Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143234
Title: On the sample complexity of multichannel frequency estimation via convex optimization
Authors: Yang, Zai
Tang, Jinhui
Eldar, Yonina C.
Xie, Lihua
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Yang, Z., Tang, J., Eldar, Y. C., & Xie, L. (2019). On the sample complexity of multichannel frequency estimation via convex optimization. IEEE Transactions on Information Theory, 65(4), 2302-2315. doi:10.1109/TIT.2018.2881113
Journal: IEEE Transactions on Information Theory
Abstract: The use of multichannel data in line spectral estimation (or frequency estimation) is common for improving the estimation accuracy in array processing, structural health monitoring, wireless communications, and more. Recently proposed atomic norm methods have attracted considerable attention due to their provable superiority in accuracy, flexibility, and robustness compared with conventional approaches. In this paper, we analyze atomic norm minimization for multichannel frequency estimation from noiseless compressive data, showing that the sample size per channel that ensures exact estimation decreases with the increase of the number of channels under mild conditions. In particular, given L channels, order K (log K) (1 + L/1 log N) samples per channel, selected randomly from N equispaced samples, suffice to ensure with high probability exact estimation of K frequencies that are normalized and mutually separated by at least 4/N. Numerical results are provided corroborating our analysis.
URI: https://hdl.handle.net/10356/143234
ISSN: 0018-9448
DOI: 10.1109/TIT.2018.2881113
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIT.2018.2881113.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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