Fast and provable algorithms for spectrally sparse signal reconstruction via low-rank Hankel matrix completion

• Published in: Applied and computational harmonic analysis Vol. 46; no. 1; pp. 94 - 121
• Main Authors: Cai, Jian-Feng, Wang, Tianming, Wei, Ke
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.01.2019
• Subjects: Composite hard thresholding operator; Iterative hard thresholding; Low rank Hankel matrix completion; Spectrally sparse signal; Iterative hard thresholding; Composite hard thresholding operator; Spectrally sparse signal; Low rank Hankel matrix completion
• ISSN: 1063-5203; 1096-603X
• DOI: 10.1016/j.acha.2017.04.004

A spectrally sparse signal of order r is a mixture of r damped or undamped complex sinusoids. This paper investigates the problem of reconstructing spectrally sparse signals from a random subset of n regular time domain samples, which can be reformulated as a low rank Hankel matrix completion problem. We introduce an iterative hard thresholding (IHT) algorithm and a fast iterative hard thresholding (FIHT) algorithm for efficient reconstruction of spectrally sparse signals via low rank Hankel matrix completion. Theoretical recovery guarantees have been established for FIHT, showing that O(r2log2⁡(n)) number of samples are sufficient for exact recovery with high probability. Empirical performance comparisons establish significant computational advantages for IHT and FIHT. In particular, numerical simulations on 3D arrays demonstrate the capability of FIHT on handling large and high-dimensional real data.