Plug-In measure-Transformed quasi-likelihood ratio test for random signal detection

Nir Halay, Koby Todros

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Recently, we developed a robust generalization of the Gaussian quasi-likelihood ratio test (GQLRT). This generalization, called measure-Transformed GQLRT (MT-GQLRT), operates by selecting a Gaussian model that best empirically fits a transformed probability measure of the data. In this letter, a plug-in version of the MT-GQLRT is developed for robust detection of a random signal in nonspherical noise. The proposed detector is derived by plugging an empirical measure-Transformed noise covariance, obtained from noise-only secondary data, into the MT-GQLRT. The plug-inMT-GQLRTis illustrated in simulation examples that show its advantages as compared to other detectors.

Original languageEnglish
Article number7894230
Pages (from-to)838-842
Number of pages5
JournalIEEE Signal Processing Letters
Volume24
Issue number6
DOIs
StatePublished - 1 Jan 2017

Keywords

  • Higher order statistics
  • Probability measure transform
  • Robust statistics
  • Signal detection

Fingerprint

Dive into the research topics of 'Plug-In measure-Transformed quasi-likelihood ratio test for random signal detection'. Together they form a unique fingerprint.

Cite this