On minimax robust detection of stationary Gaussian signals in white Gaussian noise

Wenyi Zhang, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


The problem of detecting a wide-sense stationary Gaussian signal process embedded in white Gaussian noise, in which the power spectral density of the signal process exhibits uncertainty, is investigated. The performance of minimax robust detection is characterized by the exponential decay rate of the miss probability under a Neyman-Pearson criterion with a fixed false alarm probability, as the length of the observation interval grows without bound. A stochastic suppression condition is identified for the uncertainty set of spectral density functions, and it is established that, under the stochastic suppression condition, the resulting minimax problem possesses a saddle point, which is achievable by the likelihood ratio tests matched to a so-called suppressing power spectral density in the uncertainty set. No convexity condition on the uncertainty set is required to establish this result.

Original languageEnglish (US)
Article number5773071
Pages (from-to)3915-3924
Number of pages10
JournalIEEE Transactions on Information Theory
Issue number6
StatePublished - Jun 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Library and Information Sciences


  • Error exponent
  • NeymanPearson criterion
  • minimax robustness
  • power spectral density
  • stochastic suppression
  • wide-sense stationary Gaussian processes


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