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.
['Pu Wang', 'Hongbin Li', 'Olivier Besson', 'Jun Fang']
Knowledge-aided hyperparameter-free Bayesian detection in stochastic homogeneous environments