Bayesian Rao test for distributed target detection in interference and noise with limited training data

This paper has studied the challenge of detecting a range-spread target in interference and noise when the number of training data is limited. The interference is located within a certain subspace with an unknown coordinate, while the noise follows a Gaussian distribution with an unknown covariance matrix. We concentrate on scenarios where the training data are limited and employ a Bayesian framework to find a solution. Specifically, the covariance matrix is assumed to follow an inverse Wishart distribution. Then, we introduce a Bayesian detector according to the Rao test, which has superior detection performance compared to existing detectors in certain situations, as demonstrated by both simulation experiment and real data.