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

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Abstract

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.

Keywords

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

Abbreviations

ADTFD

Adaptive directional time–frequency distribution

ADTFDs

Adaptive directional TFDs

AM–FM

Amplitude modulation–frequency modulation

AOKTFD

Adaptive optimal kernel TFD

CKD

Compact kernel distribution

EEG

Electroencephalogram

EMBD

Extended modified B-distribution

EOMP

Extended orthogonal matching pursuit

FM

Frequency modulation

IA

Instantaneous amplitude

IF

Instantaneous frequency

MP

Matching pursuit

MSE

Mean square error

TF

Time–frequency

TFDs

Time–frequency distributions

WVD

Wigner–Ville distribution

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Copyright information

© 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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