Open AccessPersymmetric subspace adaptive detection and performance analysis
Author(s) -
Gao Yongchan,
Ji Hongbing,
Zhang Nan,
Zuo Lei
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0476
Subject(s) - subspace topology , detector , covariance matrix , computer science , algorithm , false alarm , detection theory , signal subspace , gaussian , noise (video) , pattern recognition (psychology) , artificial intelligence , image (mathematics) , telecommunications , physics , quantum mechanics
This study deals with the problem of detecting a subspace signal in coloured Gaussian noise, where the subspace signal belongs to a known subspace, but with unknown coordinates. The authors exploit the persymmetric structure of the covariance matrix by a unitary transform and then devise a persymmetric subspace detector based on two‐step design procedure. By exploiting the persymmetric structure of the covariance matrix, the proposed detector can reduce training data requirements. Additionally, approximate expressions for the probabilities of false alarm and detection of the proposed detector are derived. Numerical results demonstrate that the proposed detector can offer significantly enhanced detection performance in comparison with the conventional counterparts when the amount of training data is limited.