Knowledge-aided adaptive coherence estimator in stochastic partially homogeneous environments

Pu Wang, Zafer Sahinoglu, Man On Pun, Hongbin Li, Braham Himed

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

This letter introduces a stochastic partially homogeneous model for adaptive signal detection. In this model, the disturbance covariance matrix of training signals, R, is assumed to be a random matrix with some a priori information, while the disturbance covariance matrix of the test signal, R 0, is assumed to be equal to λR, i.e., R0= λR. On one hand, this model extends the stochastic homogeneous model by introducing an unknown power scaling factor λ between the test and training signals. On the other hand, it can be considered as a generalization of the standard partially homogeneous model to the stochastic Bayesian framework, which treats the covariance matrix as a random matrix. According to the stochastic partially homogeneous model, a scale-invariant generalized likelihood ratio test (GLRT) for the adaptive signal detection is developed, which is a knowledge-aided version of the well-known adaptive coherence estimator (ACE). The resulting knowledge-aided ACE (KA-ACE) employs a colored loading step utilizing the a priori knowledge and the sample covariance matrix. Various simulation results and comparison with respect to other detectors confirm the scale-invariance and the effectiveness of the KA-ACE.

Original languageEnglish
Article number5696739
Pages (from-to)193-196
Number of pages4
JournalIEEE Signal Processing Letters
Volume18
Issue number3
DOIs
StatePublished - 2011

Keywords

  • Bayesian inference
  • generalized likelihood ratio test
  • knowledge-aided
  • partially homogeneous model

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