Knowledge-aided hyperparameter-free Bayesian detection in stochastic homogeneous environments

Pu Wang; Hongbin Li; Olivier Besson; Jun Fang

ICASSP 2016accepted0 citations

Knowledge-aided hyperparameter-free Bayesian detection in stochastic homogeneous environments

Pu Wang, Hongbin Li, Olivier Besson, Jun Fang

Abstract

This paper considers adaptive signal detection in stochastic homogeneous environments where the disturbance covariance matrix of both test and training signals, R, is assumed to be a random matrix with a priori knowledge of R. Unlike existing detectors assuming a known hyperparameter associated with R, a knowledge-aided detector with the capability of automatic weighting is considered by accounting for the uncertainty of the prior knowledge. Specifically, the generalized likelihood ratio test (GLRT) is utilized to develop the test statistic, along with the maximum marginal likelihood (MML) estimation of the hyperparameter. The proposed KA-MML-GLRT detector is evaluated by numerical simulations and the results show improved detection performance over conventional and knowledge-aided detectors, especially in the case of limited training signals and inaccurate prior knowledge.

BibTeX

@inproceedings{icassp2016_knowledgeaidedhy,
  title = {Knowledge-aided hyperparameter-free Bayesian detection in stochastic homogeneous environments},
  author = {Pu Wang and Hongbin Li and Olivier Besson and Jun Fang},
  booktitle = {ICASSP 2016},
  year = {2016}
}