Bayesian Rao test for distributed target detection in interference and noise with limited training data
Abstract
This paper has studied the problem 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 the scenarios where the training data are limited and employ a Bayesian framework to ffnd a solution. Speciffcally, the covariance matrix is assumed to follow an inverse Wishart distribution. Then, we introduce the Bayesian detector according to the Rao test, which, demonstrated by both simulation experiment and real data, has superior detection performance to the existing detectors in certain situations.
Cite
@article{arxiv.2504.13235,
title = {Bayesian Rao test for distributed target detection in interference and noise with limited training data},
author = {Daipeng Xiao and Weijian Liu and Jun Liu and Yuntao Wu and Qinglei Du and Xiaoqiang Hua},
journal= {arXiv preprint arXiv:2504.13235},
year = {2025}
}
Comments
14 pages,18 figures. This manuscript has been accepted by SCIENCE CHINA Information Sciences