Neyman-Pearson detection of Gauss-Markov signals in noise: Closed-form error exponent and properties

Youngchul Sung, Lang Tong, H. Vincent Poor

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


The performance of Neyman-Pearson detection of correlated random signals using noisy observations is considered. Using the large deviations principle, the performance is analyzed via the error exponent for the miss probability with a fixed false-alarm probability. Using the state-space structure of the signal and observation model, a closed-form expression for the error exponent is derived using the innovations approach, and the connection between the asymptotic behavior of the optimal detector and that of the Kalman filter is established. The properties of the error exponent are investigated for the scalar case. It is shown that the error exponent has distinct characteristics with respect to correlation strength: for signal-to-noise ratio (SNR) ≥ 1 the error exponent is monotonically decreasing as the correlation becomes strong whereas for < 1 there is an optimal correlation that maximizes the error exponent for a given SNR.

Original languageEnglish (US)
Pages (from-to)1354-1365
Number of pages12
JournalIEEE Transactions on Information Theory
Issue number4
StatePublished - Apr 2006

All Science Journal Classification (ASJC) codes

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


  • Autoregressive process
  • Correlated signal
  • Error exponent
  • Gauss-Markov model
  • Neyman-Pearson detection


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