Circuits, Systems, and Signal Processing

, Volume 37, Issue 8, pp 3175–3190 | Cite as

Reconstruction of Non-stationary Signals with Missing Samples Using Time–frequency Filtering

  • Nabeel Ali KhanEmail author
  • Mokhtar Mohammadi


This study proposes a new time–frequency (TF) method for the recovery of missing samples from multicomponent signals. This is achieved by a combination of a sparsity-aware TF signal analysis method with TF filtering technique. A sparsity-aware TF method overcomes distortions caused by missing samples in the TF domain. This is followed by the use of TF filtering techniques for recovery of signals. All the extracted components are then combined to recover the complete signal. The proposed method outperforms other signal recovery methods such as gradient descent algorithm and matching pursuit.


Sparse signal reconstruction Missing samples Time–frequency filtering Time–frequency distributions 



Adaptive directional time–frequency distribution


Adaptive directional TFDs


Amplitude modulation–frequency modulation


Adaptive optimal kernel TFD


Compact kernel distribution




Extended modified B-distribution


Extended orthogonal matching pursuit


Frequency modulation


Instantaneous amplitude


Instantaneous frequency


Matching pursuit


Mean square error




Time–frequency distributions


Wigner–Ville distribution


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Electrical EngineeringFoundation UniversityIslamabadPakistan
  2. 2.Department of Computer ScienceUniversity of Human DevelopmentSulaimaniyahIraq

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