Multichannel hankel matrix completion through nonconvex optimization

Shuai Zhang, Yingshuai Hao, Meng Wang, Joe H. Chow

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


This paper studies the multichannel missing data recovery problem when the measurements are generated by a dynamical system. A new model, termed multichannel low-rank Hankel matrices, is proposed to characterize the intrinsic low-dimensional structures in multichannel time series. The data recovery problem is formulated as a nonconvex optimization problem, and two fast algorithms (AM-FIHT and RAM-FIHT), both with linear convergence rates, are developed to recover the missing points with provable performance guarantees. The required number of observations is significantly reduced, compared with conventional low-rank completion methods. Our methods are verified through numerical experiments on synthetic data and recorded synchrophasor data in power systems.

Original languageEnglish (US)
Article number8338155
Pages (from-to)617-632
Number of pages16
JournalIEEE Journal on Selected Topics in Signal Processing
Issue number4
StatePublished - Aug 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering


  • Hankel matrix
  • Low-rank matrix completion
  • linear dynamic systems
  • nonconvex optimization
  • synchrophasor data


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