Optimal and suboptimal detection of Gaussian signals in noise: Asymptotic relative efficiency

Youngchul Sung, Lang Tong, H. Vincent Poor

Research output: Contribution to journalConference article

3 Scopus citations

Abstract

The performance of Bayesian detection of Gaussian signals using noisy observations is investigated via the error exponent for the average error probability. Under unknown signal correlation structure or limited processing capability it; is reasonable to use the simple quadratic detector that is optimal in the case of an independent and identically distributed (i.i.d.) signal. Using the large deviations principle, the performance of this detector (which is suboptimal for non-i.i.d. signals) is compared with that of the optimal detector for correlated signals via the asymptotic relative efficiency defined as the ratio between sample sizes of two detectors required for the same performance in the large-sample-size regime. The effects of SNR on the ARE are investigated. It is shown that the asymptotic efficiency of the simple quadratic detector relative to the optimal detector converges to one as the SNR increases without bound for any bounded spectrum, and that the simple quadratic detector performs as well as the optimal detector for a wide range of the correlation values at high SNR.

Original languageEnglish (US)
Article number591002
Pages (from-to)1-12
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume5910
DOIs
StatePublished - Dec 1 2005
EventAdvanced Signal Processing Algorithms, Architectures, and Implementations XV - San Diego, CA, United States
Duration: Aug 2 2005Aug 4 2005

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Keywords

  • Asymptotic relative efficiency (ARE)
  • Error exponent
  • Large deviations principle
  • Quadratic detector

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