Quasi maximum likelihood estimator of polynomial phase signals for compressed sensed data

Igor Djurović, Vladimir V. Lukin, Marko Simeunović, Braham Barkat

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Several papers in the literature cover parameter estimation of frequency modulated (FM) signals under reduced number of signal samples with respect to the Nyquist/Shannon criterion, i.e., within the compressive sensing (CS) framework. However, scope of these papers is mainly limited to sinusoids or sum of sinusoids. In this paper, the CS framework is extended to parameter estimation of higher order polynomial phase signals (PPSs) using the quasi-maximum likelihood (QML) estimator and robust short-time Fourier transform (STFT). The considered signal is assumed to be non-uniformly sampled PPS with smaller number of samples with respect to the Nyquist/Shannon criterion. However, the proposed technique can also be generalized to uniformly sampled signals with missing or unreliable samples.

Original languageBritish English
Pages (from-to)631-636
Number of pages6
JournalAEU - International Journal of Electronics and Communications
Volume68
Issue number7
DOIs
StatePublished - Jul 2014

Keywords

  • Compressive sensing
  • Parameter estimation
  • Polynomial phase signals

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