Adaptive Detection of a Partly Known Signal Corrupted by Strong Interference

Research output: Contribution to journalArticle

Abstract

In this letter, we consider adaptive detection of a partly known signal corrupted by additive noise and strong interference with support that is only partly known. Assuming a homogeneous environment where the covariance matrix of the additive noise is the same for the primary and secondary data sets, although with the secondary data set also being affected by the interference, we allow for conic uncertainty models for both the signal and interference subspaces, developing a generalized likelihood ratio detector for the signal of interest. Numerical examples indicate that the proposed method offers a notable performance gain as compared to other recent related methods.

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Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Probability Theory and Statistics

Keywords

  • uncertainty, strong interference, signal detection, Iterative methods
Original languageEnglish
Pages (from-to)729-732
JournalIEEE Signal Processing Letters
Volume18
Issue number12
Publication statusPublished - 2011
Publication categoryResearch
Peer-reviewedYes