Open AccessExploiting persymmetry for JDL‐STAP
Author(s) -
Wang Sha,
Shi Bo,
Hao Chengpeng,
Liu Minggang,
Xu Da
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0204
Subject(s) - space time adaptive processing , computer science , clutter , covariance matrix , radar , joint (building) , domain (mathematical analysis) , algorithm , training set , artificial intelligence , engineering , mathematics , radar engineering details , telecommunications , radar imaging , architectural engineering , mathematical analysis
Joint domain localisation (JDL) is a popular reduced‐dimension space‐time adaptive processing (STAP) technique for clutter suppression in an air‐borne radar system. Here, the authors develop an improved JDL method by exploiting persymmetric covariance matrix, referred to as persymmetric joint domain localisation (Per‐JDL), in order to make maximum use of training samples and further improve the STAP performance under small training data support. The proposed algorithm is verified to be efficient in training‐limited scenarios by simulation results.