Detection of a rank-one signal with limited training data
Abstract
In this paper, we reconsider the problem of detecting a matrix-valued rank-one signal in unknown Gaussian noise, which was previously addressed for the case of sufficient training data. We relax the above assumption to the case of limited training data. We re-derive the corresponding generalized likelihood ratio test (GLRT) and two-step GLRT (2S--GLRT) based on certain unitary transformation on the test data. It is shown that the re-derived detectors can work with low sample support. Moreover, in sample-abundant environments the re-derived GLRT is the same as the previously proposed GLRT and the re-derived 2S--GLRT has better detection performance than the previously proposed 2S--GLRT. Numerical examples are provided to demonstrate the effectiveness of the re-derived detectors.
Keywords
Cite
@article{arxiv.2105.13282,
title = {Detection of a rank-one signal with limited training data},
author = {Weijian Liu and Zhaojian Zhang and Jun Liu and Zheran Shang and Yong-Liang Wang},
journal= {arXiv preprint arXiv:2105.13282},
year = {2021}
}
Comments
This manuscript is accepted by Signal Processing