Open AccessRobust space‐time adaptive processing based on covariance matrix reconstruction and steering vector correction
Author(s) -
Hu Xueyao,
Zhang Xinyu,
Li Yang,
Wang Hongyu,
Wang Yanhua
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0708
Subject(s) - covariance matrix , clutter , space time adaptive processing , computer science , algorithm , radar , matrix (chemical analysis) , covariance , control theory (sociology) , artificial intelligence , mathematics , radar engineering details , radar imaging , telecommunications , statistics , materials science , control (management) , composite material
Clutter presents considerable heterogeneity in forward‐looking airborne radar (FLAR) applications and conventional space‐time adaptive processing (STAP) methods are sensitive to model mismatch. As a result, when a strong target signal contaminates the training samples, despite the use of guard cells, the performance of conventional STAP methods degrades significantly. In this study, a robust method, which involves reconstructing a target‐free covariance matrix and correcting the presumed steering vector to prevent target cancellation in FLAR, is proposed. First, the target‐free covariance matrix is reconstructed through integrating the spatial–temporal spectrum over a sector separated from the desired frequency and direction of targets. Subsequently, the mismatch between presumed steering vector and actual steering vector is corrected via quadratic optimisation. In addition, the processing scheme is applied to real‐measured clutter data, and the experimental results validate the effectiveness of the proposed method.