Open AccessPersymmetric Rao and Wald tests for adaptive detection of distributed targets in compound‐Gaussian noise
Author(s) -
Guo Xiaolu,
Tao Haihong,
Zhao HongYan,
Liu Jun
Publication year - 2017
Publication title -
iet radar, sonar and navigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.489
H-Index - 82
eISSN - 1751-8792
pISSN - 1751-8784
DOI - 10.1049/iet-rsn.2016.0251
Subject(s) - wald test , covariance matrix , detector , gaussian noise , gaussian , noise (video) , covariance , algorithm , computer science , matrix (chemical analysis) , gaussian elimination , mathematics , statistical hypothesis testing , statistics , artificial intelligence , physics , telecommunications , quantum mechanics , image (mathematics) , materials science , composite material
The problem of detecting a distributed target in the presence of compound‐Gaussian noise with unknown covariance matrix is studied in this paper. Since no uniformly most powerful test exists for the problem at hand, two detectors based on the Rao and Wald tests are devised. Remarkably, the persymmetric structure of the covariance matrix is exploited in the design of the proposed detectors. Simulation results show that the proposed detectors outperform the traditional detectors, especially in training‐limited scenarios.